Jyväskylä Summer School Course Programme

BIO1: MicroRNA Biology and Function

Time: 4. - 6.8.2026 (NB! The course lasts 3 days, Mon-Wed)
Study mode: In person 
Max. number of participants:  40 
Lecturers: Maija Puhka (Helsinki), Matthias Hackl (Wien, Austria), Sira Karvinen (Helsinki/Jyväskylä), Tia-Marje Korhonen (Jyväskylä), Tiina Jokela (Jyväskylä), Moona Huttunen (Jyväskylä), Arto Mannermaa (Kuopio), Eija Laakkonen (Jyväskylä)
Coordinators: Moona Huttunen and Tiina Jokela
Course code: BENS7011
Modes of study: Lectures 
Credits: 1 ECTS
Evaluation: Pass/Fail
Contents: This course develops a comprehensive understanding of microRNA biology, with a focus on their molecular mechanisms, functional diversity, and translational applications. Students will explore how microRNAs and their isomiR variants regulate gene expression, participate example in ageing processes, and serve as robust biomarkers in cancer. Through case studies such as the EV-miRNA field and the commercial success story of TAmiRNA, the course also illustrates how basic research can evolve into clinically relevant innovations. In addition, students will gain practical insight into the analysis of miRNA and isomiR data, preparing them to critically evaluate and apply small RNA research in various biological contexts.    
Learning outcomes: After completing this course, the student:

• Understands the core principles of microRNA biology and the mechanisms regulating miRNA function.
• Recognizes different carriers and transport mechanisms of microRNAs, including extracellular vesicles.
• Understands isomiR expression, its biological relevance, and the fundamentals of isomiR expression data analysis.
• Is able to connect microRNA biology to diverse biological processes such as ageing and cancer development.
• Can describe how microRNAs function as biomarkers in health and disease and evaluate their translational potential.

Prerequisites: Basic courses in biochemistry and molecular biology.

BIO2: Identification and Ecology of Aquatic Macrophytes

Time: 3.8. - 7.8.2026
Study mode: In person 
Max. number of participants:  14 
Lecturer: Krister Karttunen (Syke, Finland)
Coordinator: Heikki Hämäläinen
Course code: WETS1058
Modes of study: Lectures, demonstration of macrophyte survey methods on the shore of the lake adjacent to university buildings, and a field excursion by bus to representative stream and lake sites, identification of macrophytes in the field and of samples in the lab.
Credits: 2 ECTS
Evaluation: 0-5
Contents: Lectures on macrophyte biology, demonstration and exercising of survey methods in the field, species identification in the field and lab.
Learning outcomes:  After completing this course, the student should know the basics of macrophyte biology and their environmental relationships, how to survey macrophyte vegetation in lakes and streams, and should actively know the core species of macrophytes in boreal waters and be capable of identifying all species when needed. 
Prerequisites: BSc in aquatic, environmental science or related field 

BIO3: Phytoplankton Identification

Time: 10.8. - 14.8.2026
Study mode: In person 
Max. number of participants: 16 
Lecturers: Kristiina Vuorio (Syke, Finland), Katja Pulkkinen (JYU) and Minna Hiltunen (JYU)
Coordinator: Katja Pulkkinen
Course code: WETS1054
Modes of study: Lectures, phytoplankton sampling from the shore of the lake adjacent to university buildings, sample preparation, hands on identification of phytoplankton samples with an inverted microscope working in pairs 
Credits: 2 ECTS
Evaluation: Pass/Fail
Contents: Demonstrations and practical exercises to introduce the main types of freshwater phytoplankton and their identification. 
Learning outcomes: After completing this course, the student should know how to collect phytoplankton samples and how to prepare them for sample analysis. They know the identification and counting techniques, recognize the higher phytoplankton taxa and are capable of species identification. 
Prerequisites: BSc in aquatic, environmental science or related field, with basic knowledge on use of microscopes. 

CH1: Problem-Led Investigation: Navigating the Journey from Analytical Design to Hands-On Implementation for Multidisciplinary Sciences

Time:  3.8.-7.8.2026 
Study mode:  In person
Max. number of participants: 20
Lecturers: Ian Scowen (University of Lincoln, UK) and Tasnim Munshi (University of Keele, UK)   
Coordinator: Elina Laurila
Course code: KEMS9621
Modes of study: Lectures, Workshop, Hands-on Lab Showcase Training, Group Project Work, Presentation, Independent study  
Credits: 2 ECTS
Evaluation: Pass/fail
Contents: Problem-Led Investigation: Navigating the Journey from Analytical Design to Hands-On Implementation in Multidisciplinary Sciences is an immersive, practice-focused short course designed to develop confident, problem-led analytical thinking across a range of scientific contexts. The course emphasises how modern analytical methods can be used strategically to address real-world problems, moving from initial problem definition through analytical design to practical implementation and interpretation.

Through interactive lectures, participants are introduced to the principles underpinning analytical strategy, project planning and execution, alongside a working understanding of modern elemental, molecular and materials analysis techniques. The focus is firmly on what these techniques can deliver in practice, including their strengths, limitations and common challenges, rather than on extensive theoretical derivations. Realistic problem scenarios drawn from forensic, environmental, healthcare and consumer science provide a consistent framework for learning and discussion.

Participants work in small, supportive teams to plan and develop a group investigation, supported by structured round-table sessions that encourage collaborative analysis, peer-learning and shared problem solving. Daily reflective “wash-up” sessions provide structured time for critical evaluation of data, review of project progress, effective data presentation and guided interpretation.

By the end of the course, participants will have developed greater confidence in designing and executing analytical strategies, critically evaluating data, and understanding the capabilities and limitations of modern analytical techniques. They will leave better equipped to apply analytical thinking, collaborative problem-solving and evidence-informed judgement to multidisciplinary scientific challenges in their own study or professional practice. 
Learning outcomes: 

•    Analyse real-world scientific problems and formulate appropriate analytical questions across multidisciplinary contexts.
•    Design fit-for-purpose analytical strategies, selecting suitable elemental, molecular and materials analysis techniques while recognising their capabilities and limitations.
•    Apply principles of project planning and execution to collaborative, problem-led investigations.
•    Critically evaluate analytical data, including assessment of data quality, uncertainty and limitations, to support informed interpretation.
•    Communicate analytical findings effectively through appropriate data representation, discussion and peer-based review.
•    Demonstrate effective teamwork, collaborative problem-solving and reflective practice in a supportive laboratory and discussion-based environment. 

Prerequisites: B.Sc. or equivalent in chemistry  

NANO2/CH2: History of Science: Alchemy as a Predecessor of Modern Chemistry

Time: 10. - 14.8.2026 
Study mode: In person 
Max. number of participants:  - 
Lecturer: Prof. Lawrence Principe (Johns Hopkins University, USA)   
Coordinator: Markus Ahlskog 
Course code: NANS7017
Modes of study: Lectures and group excercises
Credits: 1 ECTS
Evaluation: Pass/Fail
Contents: Although often misunderstood as a kind of magic or superstition, alchemy is now recognized by historians as an important contributor to the development of modern science. We now know that alchemists based their work on empirically-derived theories of matter, established principles central for modern chemistry, stressed the power of human abilities to imitate and exceed natural processes, and helped establish an experimental approach to nature. This course will cover the history of alchemy in Europe from its establishment as a Latin science in the Late Middle Ages through its multiple developments down to the Scientific Revolution. It will examine the theoretical and practical content of alchemy, both its metallic (for example, the search for the philosophers’ stone and goldmaking) and its medical dimensions. We will explore how and why alchemists came to believe what they did, and how they were viewed by their contemporaries. Alchemists often wrote in a coded and secret language, and we will work to decipher their often-strange writings and imagery in order to understand their thinking and experiences. A special feature will be the lecturer’s presentation of his own experimental reconstructions of alchemical processes. The alchemical pursuits and ideas of both little-known and famous figures (such as Robert Boyle and Isaac Newton) will be studied, as well as alchemy’s “transmutation” into chemistry, highlighting what the emergence of modern chemistry owes to alchemy.   
Learning outcomes:  Students will learn:

• the worldview of the alchemists and how it was justified
• how organized theories can emerge from experience and experiment
• the alchemical origins of fundamental chemical principles
• how scientific developments are often guided by “non-scientific” factors
• how to contextualize scientific ideas in their historical place

Prerequisites: -

PH1: Exploring the Invisible Universe: Neutrinos, Dark Matter, and Cosmic Structure    

Time: 10. - 14.8.2026
Study mode: In person
Max. number of participants: -
Lecturer: Mariia Fedkevych 
Coordinators: Hannu Paukkunen and Ilkka Helenius
Course code: FYSJ5111   
Modes of study: Lectures + exercises     
Credits: 1 ECTS
Evaluation : Pass/fail
Contents: Our Universe is shaped by particles we can barely detect and phenomena we still struggle to understand. This course introduces students to two of the most elusive components of the cosmos—neutrinos and dark matter—and explores their critical roles in cosmic evolution and structure formation. Over the course, we will discuss the fundamental physics of neutrinos and dark matter, the (astro)physical evidence for their existence, detection principles and the current experimental efforts to study them. The course is designed to give participants a broad, coherent picture of how these “invisible” particles can illuminate some of the biggest open questions in modern physics.   
Learning outcomes: After completing the course, the student will:

• understand the role of neutrinos and dark matter in the evolution and large-scale structure of the Universe;
• be able to explain the basic properties of neutrinos and dark matter candidates from both theoretical and experimental perspectives;
• become familiar with key detection techniques used in neutrino and dark matter experiments;
• be able to interpret simple observational or experimental signatures related to astroparticle physics;
• gain an overview of current challenges and open questions in neutrino (astro)physics and dark matter research.

Prerequisites: Familiarity with basic particle physics concepts and special relativity.    

PH2: Basics of Jet Substructure

Time: 3. - 7.8.2026   
Study mode: In person
Max. number of participants: -
Lecturer: Oleh Fedkevych (JYU)
Coordinators: Hannu Paukkunen and Ilkka Helenius
Course code: FYSJ5112   
Modes of study: Lectures + hands-on expercises      
Credits: 1 ECTS
Evaluation: Pass/fail
Contents: In these lectures, I will introduce students to the foundations of jet substructure physics. In particular, I will discuss the production of hadronic jets at the LHC, explain the main ideas and concepts used to construct jet substructure observables, and provide an overview of the most important scientific results obtained with jet substructure studies.
After that, I will introduce jet grooming techniques (such as SoftDrop and CollinearDrop), which allow us to study different jet-production regimes separately (via collinear and soft emissions), and explain how these techniques can be used to perform precise tests of Quantum Chromodynamics (QCD). Finally, I will explain the basics of resummation techniques used to calculate the cumulative distribution of a simple jet substructure observable at leading- and next-to-leading-log accuracy levels.  During the hands-on session, the students will compare the obtained theoretical predictions against experimental jet mass measurements performed by the CMS collaboration
Learning outcomes: Students will receive a broad overview of jet substructure physics and of the most important results obtained in the field. They will also master the basics of resummation techniques used to calculate cumulative distributions of jet mass at leading- and next-to-leading-log accuracy levels.
Prerequisites: Basic knowlege of paritcle physics. Laptop / PC with interntet connection and personal Google collab account (for the hands-on sessions).    

PH3: Deep Inelastic Scattering Revisited

Time:  3. - 7.8.2026   
Study mode: In person
Max. number of participants: -
Lecturer: Felix Hekhorn (JYU)
Coordinators: Hannu Paukkunen and Ilkka Helenius
Course code: FYSJ5113
Modes of study: Lectures + exercises
Credits: 1 ECTS
Evaluation: Pass/fail
Contents: Due to its conceptual simplicity, the deep inelastic scattering (DIS) often serves as a textbook example for Quantum Chromo Dynamics (QCD) and partonic structure of nucleons. However, it is this supposed simplicity, which makes DIS an ideal case for discussing many additional effects and corrections. In these lectures we discuss DIS within the domain of perturbative QCD covering a range of topics ranging from mathematical problems to kinematical corrections, and to practical challenges when attempting interpret experimental measurements. Although these lectures focus on the exemplary case of DIS, the discussed topcis are relevant to a larger class of processes. We also overview existing and future DIS measurements, and discuss their implementation into modern extractions of parton distribution functions (PDFs) which quantify the partonic structure of nucleons.
Learning outcomes: 
•    QCD effects beyond standard textbook knowledge
•    necessary steps to actually use real-world DIS data
•    usage of DIS measurements in perturbative QCD
Prerequisites: Basic knowledge of quantum field theory and particle physics

PH4: Radiation Detection and Dosimetry 

Time: 10. - 14.8.2026
Study mode: In person 
Max. number of participants: 15 
Lecturers: Prof.Teemu Siiskonen (STUK and University of Helsinki, Finland) and Erik Brücken, (University of Helsinki, Finland)
Coordinator: Sami Räsänen
Course code: FYSJ5114 
Modes of study: Lectures, exercises, laboratory work, group work     
Credits:  2 ECTS
Evaluation: Pass/Fail
Contents: The physics of ionizing radiation with a special emphasis on basic quantities and the physics of dosimetry. We introduce cavity theory and its applications, and discuss how to estimate the exposure in different cases. The course contains hands-on exercises in the laboratory where different kinds of radiation detectors are demonstrated and their characteristics explained, building understanding of the advantages and limitations of various detector technologies in given applications.  
Learning outcomes:    

• Student can use fundamental dosimetry tools in different applications and can estimate measurable doses and exposure of people in various cases.
• Explain physics of ionizing radiation.
• Explain the physics principles of radiation detectors.
• Distinguish between different types of detectors.
• Present aspects of semi-conductor radiation detectors (same for gaseous and scintillator-based detectors).
• Apply detectors for studying ionising radiation.
• Experiment with detectors in the laboratory (hands-on experiences).
• Describe and apply electronic data acquisition to readout radiation detectors.
• Analyze data from experiments and compare own results to those of fellow students.

Prerequisites: Basic knowledge on ionizing radiation and electronics, as typically obtained in first master level course on nuclear-, particle- or material physics and a course on electronics.  
 

NANO3/PH5: From Classical to Quantum Mechanics

Time: 10. - 14.8.2026
Study mode: In person
Max. number of participants: -
Lecturer:  Prof. Ismo Koponen (University of Helsinki, Finland)
Coordinator: Riku Tuovinen
Course code: NANS7018
Modes of study: Lectures and group exercises
Credits: 1 ECTS
Evaluation: Pass/fail
Contents: The course follows the transition from classical to quantum mechanics, beginning with Planck's quantization and the early attempts to preserve classical reasoning. We examine the key problem cases that exposed the limits of classical ideas, including the Compton effect and the development of atomic theory through Bohr and Sommerfeld, with emphasis on scientific logic and the role of models, approximations, and experimental evidence during the transition period. The course concludes with the decisive conceptual shift to Heisenberg's matrix mechanics, and with Schrödinger's later reflections on entanglement that opened a new way of thinking about what physical theory can say.
Learning outcomes: After passing the course, students should have an understanding of events that have led to quantum modern physics, mutual interrelationships between different actors in developing the new physics. Students also have an overall idea of the cultural context in which new ideas emerged. The evaluation is based on three exercises (in groups) during the course.
Prerequisites: Bachelor level physics

• Basic knowledge of classical mechanics, electromagnetism, quantum mechanics

NANO1: Theory and Simulation of Electrochemical Electron Transfer Kinetics 

Time: 10. - 14.8.2026
Study mode: In person 
Max. number of participants: 20
Lecturers: Prof. Jun Huang (Jülich, Germany) and Marko Melander (JYU)     
Coordinator: Marko Melander
Course code: NANS7016
Modes of study: Lectures, workshop, demos, discussion, and reading
Credits:  2 ECTS
Evaluation: Pass/Fail
Contents: Electron transfer (ET) at electrochemical interfaces lies at the heart of numerous energy conversion and storage processes, including fuel cells, batteries, and CO₂ reduction technologies. Despite its conceptual foundations being established decades ago, the theoretical treatment of ET remains a complex and evolving subject, particularly due to the intricate coupling between classical solvent fluctuations and quantum electronic states of metal electrodes and redox species, as well as the inherent complexity of solvent dynamics and electric double layer (EDL) effects. A comprehensive understanding of how these factors collectively influence ET kinetics is fundamental to optimizing electrochemical energy conversion and storage devices.
In recent years, advances in atomistic simulation techniques—such as density functional theory (DFT) and molecular dynamics (MD) simulations—have enabled increasingly detailed and accurate microscopic descriptions of electrochemical ET. These methods have provided critical mechanistic insights into interfacial charge transfer processes. However, due to their high computational cost, they are typically limited to small, idealized systems and relatively short timescales, which restricts their direct applicability to experimental or technologically relevant conditions.
A promising strategy to overcome these limitations is to combine physically motivated conceptual models with parameters extracted from atomistic simulations, thereby integrating microscopic accuracy with theoretical scalability; establishing this connection is the central aim of this course, which will:
1) Provide a unified conceptual and theoretical framework for electrochemical ET 
(2) Parameterize conceptual theory with atomistic simulations. 
(3) Integrate ET and EDL theories to achieve a more comprehensive and realistic modeling of electrochemical ET kinetics. 
(4) Assess the limitations and advances the theoretical and computational modeling of electrochemical ET. 
The course is largely based on a review article the lecturers have submitted to Chemical Reviews (arxiv preprint: https://doi.org/10.48550/arXiv.2510.24635). The course covers topics such as: 

•  General chemical rate theory and transition state theory
• Rate theory for electron transfer (ET) reactions
• Marcus theory for molecular and electrochemical ET kinetics
• Atomistic simulation of ET rates
• Effective, physical models of ET kinetics
• Solvent dynamics and non-adiabatic effects
• Influence of the reaction environment on ET rates: Frumkin corrections and beyond
• Hierarchical modeling of electrocatalytic reactions
• Open questions and future directions in ET theory and simulation

Learning outcomes:    
•    Understand the connections between general rate theory and electron transfer kinetics
•    Learn the central factors controlling electron transfer kinetics and how they are described theory theoretical methods
•    Learn the key equations and their merits/limitations for describing electron transfer kinetics 
•    Design and analyze atomistic simulations to parametrize the key equations 
Prerequisites: Basics of statistical thermodynamics/physics, basics of atomistic simulations, basics of electrochemistry      

NANO2/CH2: History of Science: Alchemy as a Predecessor of Modern Chemistry

Time: 10. - 14.8.2026 
Study mode: In person 
Max. number of participants:  - 
Lecturer: Prof. Lawrence Principe (Johns Hopkins University, USA)   
Coordinator: Markus Ahlskog 
Course code: NANS7017
Modes of study: Lectures and group excercises
Credits: 1 ECTS
Evaluation: Pass/Fail
Contents: Although often misunderstood as a kind of magic or superstition, alchemy is now recognized by historians as an important contributor to the development of modern science. We now know that alchemists based their work on empirically-derived theories of matter, established principles central for modern chemistry, stressed the power of human abilities to imitate and exceed natural processes, and helped establish an experimental approach to nature. This course will cover the history of alchemy in Europe from its establishment as a Latin science in the Late Middle Ages through its multiple developments down to the Scientific Revolution. It will examine the theoretical and practical content of alchemy, both its metallic (for example, the search for the philosophers’ stone and goldmaking) and its medical dimensions. We will explore how and why alchemists came to believe what they did, and how they were viewed by their contemporaries. Alchemists often wrote in a coded and secret language, and we will work to decipher their often-strange writings and imagery in order to understand their thinking and experiences. A special feature will be the lecturer’s presentation of his own experimental reconstructions of alchemical processes. The alchemical pursuits and ideas of both little-known and famous figures (such as Robert Boyle and Isaac Newton) will be studied, as well as alchemy’s “transmutation” into chemistry, highlighting what the emergence of modern chemistry owes to alchemy.   
Learning outcomes:  Students will learn:

• the worldview of the alchemists and how it was justified
• how organized theories can emerge from experience and experiment
• the alchemical origins of fundamental chemical principles
• how scientific developments are often guided by “non-scientific” factors
• how to contextualize scientific ideas in their historical place

Prerequisites: -

NANO3/PH5: From Classical to Quantum Mechanics

Time: 10. - 14.8.2026
Study mode: In person
Max. number of participants: -
Lecturer:  Prof. Ismo Koponen (University of Helsinki, Finland)
Coordinator: Riku Tuovinen
Course code: NANS7018
Modes of study: Lectures and group exercises
Credits: 1 ECTS
Evaluation: Pass/fail
Contents: The course follows the transition from classical to quantum mechanics, beginning with Planck's quantization and the early attempts to preserve classical reasoning. We examine the key problem cases that exposed the limits of classical ideas, including the Compton effect and the development of atomic theory through Bohr and Sommerfeld, with emphasis on scientific logic and the role of models, approximations, and experimental evidence during the transition period. The course concludes with the decisive conceptual shift to Heisenberg's matrix mechanics, and with Schrödinger's later reflections on entanglement that opened a new way of thinking about what physical theory can say.
Learning outcomes: After passing the course, students should have an understanding of events that have led to quantum modern physics, mutual interrelationships between different actors in developing the new physics. Students also have an overall idea of the cultural context in which new ideas emerged. The evaluation is based on three exercises (in groups) during the course.
Prerequisites: Bachelor level physics

• Basic knowledge of classical mechanics, electromagnetism, quantum mechanics
   

MA1: Nonlinear Fokker-Planck Flows and their Probabilistic Counterparts 

Time: 3. - 6.8.2026
Study mode: In person. 
Max. number of participants: Not applicable
Lecturer: Professor Michael Roeckner (University of Bielefeld, Germany)
Coordinator: Stefan Geiss 
Course Code: MATJ5126 
Modes of study: Lectures and homework      
Credits: 2 ECTS   
Evaluation:  Pass/fail
Contents: Already in 1966 in his visionary paper in PNAS, H.P. McKean, jr., formulated a programme to construct a probabilistic counterpart to nonlinear parabolic partial differential equations (PDEs) in the form of nonlinear Markov processes, in the same way as was being done at that time in the linear case. The aim was to exploit this relationship to transform problems in analysis to their probabilistic counterparts and vice versa as well as to have two associated tool boxes at hand for their better understanding and for developing respective solution strategies in both fields. While the linear theory was widely developed in the past 60 years with great success, documented in a huge literature up to today, McKean’s nonlinear case was, however, much less developed and for quite some time many standard nonlinear parabolic PDEs were not covered because of too strong assumptions on the coefficients. Starting from around 2018 the situation substantially changed and by employing a new technique, that is, the (nonlinear) superposition principle, the said restrictions on the coefficients could be considerably weakened and a number of nonlinear parabolic PDEs, such as the viscous Burgers equation, the generalized (possibly in space nonlocal) porous media equations, 2D vorticity Navier-Stokes equations and, more recently, the (doubly nonlinear) Leibenson equation, could be shown to have a nonlinear Markov process as its probabilistic counterpart. The Leibenson equation contains the parabolic p-Laplace equation as a special case, in which one thus obtains a complete analogue of classical Brownian motion, which is the linear Markov process associated to the classical heat equation (= parabolic 2-Laplace equation), namely the p-Brownian motion as the nonlinear Markov process associated to the parabolic p-Laplace equation. In this lecture course the underlying general technique will be presented, i.e.,
(i) Identify the nonlinear parabolic PDE as a nonlinear Fokker-Planck-Kolmogorov equation (FPKE) and solve it;
(ii) Solve the corresponding McKean-Vlasov stochastic differential equation (MVSDE) by linearization and applying the superposition principle;
(iii) Prove that the path laws of the solutions to the MVSDE (for a suitable class of initial conditions) form a nonlinear Markov process in the sense of McKean;
Obviously, a crucial point to implement this technique is to construct solutions to nonlinear FPKEs in (i). For illustration a corresponding general existence theorem including its proof, which applies to quite a large class of FPKEs, will also be part of the lecture course.

References:
Barbu/Rehmeier/R: arXiv: 2409.18744v2, AOP 2025+
Barbu/Grube/Rehmeier/R: arXiv: 2508.12979
Barbu/R: Springer LN 2024
Barbu/R/Deng Zhang: arXiv: 2309.13910, JEMS 2025+
Barbu/R: PTRF 2024
Barbu/R: AOP 2020 and SIAM 2018
McKean: PNAS 1966
Trevisan: EJP 2016  

Learning outcomes: Among other things the students will learn:
(a) A fundamental way how to connect analysis and probability;
(b) How to solve a fairly large class of nonlinear parabolic PDEs;
(c) How to solve McKean-Vlasov SDEs with merely measurability conditions on the coefficients (in particular, in their probability measure-valued variable);
(d) About the (in probability fundamental) notion of a (linear and) nonlinear Markov process;
(e) How to prove the crucial Markov property without having well-posedness, neither for the considered nonlinear parabolic PDE (FPKE) nore the associated McKean-Vlasov SDE.   
Prerequisites: 
- basic knowledge in probability and measure theory;
- basic knowledge in stochastic analysis (Itô-formula, weak solutions to SDEs, martingale problem);
- basic knowledge in functional analysis related to linear PDEs (Hilbert spaces, weak topology, Lax-Milgram theorem, Schauder fix point theorem, Sobolev embeddings)  

 

MA2: Geometric Measure Theory for the Evolution of Dislocations

Time: 3. - 7.8.2026
Study mode: In person
Participants: -
Lecturer: Filip Rindler (University of Warwick, UK)
Coordinator: Danka Lucic
Course code: MATJ5127  
Modes of study: The course consists of 10 hours of lectures + one or two exercise sessions and it can be passed by solving given exercise problems.   
Credits: 2 ECTS
Evaluation: Pass/fail
Contents: The motion of dislocations, i.e. topological defects in a crystal lattice, constitutes the predominant microscopic mechanism enabling the plastic deformation of crystalline solids (such as metals). Because only low regularity is to be expected of these lines and because topological changes may occur along the flow, integral and normal currents have long been identified as the natural mathematical objects to model these phenomena. One can think of them as measure-theoretic versions of geometric objects like curves or surfaces. While the stationary (time-independent) theory has been well understood for some time, the study of evolutions is more recent. In particular, over the last couple of years such questions have been investigated via a variational approach in space-time as well as through the so-called geometric (Lie) transport equation. These lectures will give an introduction to these approaches with a view to unsolved theoretical challenges as well as applications from the realm of material science.
Topics:
1. Introduction and physical motivation
2. Integral and normal currents
3. Variational theory of space-time integral currents
4. The geometric derivative
5. Applications
Learning outcomes: 
- to see how low-regularity geometric objects arise naturally in material science modelling
- to learn some basics of the theory of currents
- to understand how the evolution of geometric objects can be described variationally and via the geometric transport equation
- to be able to use these methods in the study of applied problems
Prerequisites: Just a good undergraduate knowledge of analysis and measure theory - no knowledge of currents or geometric analysis will be required!

MA3: Curvature Bounds and Nilpotent Structures

Time: 3. - 7.8.2026
Study mode: In person
Participants: -
Lecturer: Elia Bruè (Bocconi University, Milan)
Coordinator: Enrico Pasqualetto
Course code: MATJ5128   
Modes of study: The course consists of 10 hours of lectures and it can be passed by solving given exercise problems. 
Credits: 2 ECTS
Evaluation: Pass/fail
Contents: A central theme in Riemannian geometry is the study of the interplay between curvature and topology. Particularly relevant is the investigation of algebraic restrictions on the fundamental groups of manifolds satisfying different curvature bounds. Starting from Gromov’s celebrated almost flat theorem for manifolds with bounded sectional curvature, and the Fukaya–Yamaguchi almost nilpotency theorem for spaces with lower sectional curvature bounds, it has become clear that nilpotent structures play a fundamental role in this context. The aim of this course is to review these classical results and key examples, and then to focus on more recent developments involving Ricci curvature bounds, including progress and perspectives related to the Milnor conjecture.     
Learning outcomes: After completing the course, the student will:
- Understand the relationship between curvature bounds and algebraic properties of fundamental groups, with particular emphasis on nilpotent and abelian structures.
- Be familiar with foundational results such as Gromov’s almost flat theorem and the Fukaya–Yamaguchi almost nilpotency theorem, and understand their role within the broader landscape of Riemannian geometry. 
- Gain exposure to modern techniques, current research directions, and open problems in the study of Ricci curvature and topology, and develop the background needed to approach contemporary research literature in the field.
Prerequisites: Basic Riemannian geometry (complete manifolds, connections, geodesics, curvature) and algebraic topology (fundamental groups and covering spaces).

IP1: Electrical Impedance Tomography: Computation and Applications 

Time: 10. - 14.8.2026
Study mode: In person
Max. number of participants: -
Lecturer: Melody Alsaker (Gonzaga University, United States)
Coordinators: Janne Nurminen and Joonas Ilmavirta
Course code: MATJ5129
Modes of study: Lectures (and project work for those who want credits)
Credits: 2 ECTS
Evaluation: Project work pass/fail
Contents: This course focuses on the applied and computational aspects of electrical impedance tomography (EIT), including modeling, EIT systems, reconstruction algorithms, and hands-on MATLAB implementation. We will examine how EIT is used in biomedical, industrial, and geophysical settings, how modeling choices influence image quality, and how to interpret reconstructed conductivity images. Modern reconstruction methods, including the use of Complex Geometrical Optics solutions in the direct D-bar method, will be explored both conceptually and through MATLAB demonstrations. It is recommended that participants bring a laptop with MATLAB installed, although a computer lab will be available. Recommended to take together with Mathematics of Electrical Impedance Tomography.
Learning outcomes: Insight into practical EIT modeling, applications, and reconstruction algorithms.
Prerequisites: Basics of linear algebra and numerical methods, introductory exposure to PDEs, and basic programming skills (preferably in MATLAB)
 

IP2: Mathematics of Electrical Impedance Tomography

Theme: Probability Theory
Time: 10.-14.8.2026
Study mode: In person
Max. number of participants: -
Lecturer: Samuli Siltanen (University of Helsinki, Finland)
Coordinator: Janne Nurminen and Joonas Ilmavirta
Code: MATJ5130
Modes of study: Lectures (and project work for those who want credits)
Credits: 2 ECTS
Evaluation: Project work pass/fail
Contents: This course focuses on mathematical aspects of electrical impedance tomography (EIT). A simple pixel-based diffusion model serves as a gentle introduction to the principle of EIT measurement, illustrating key challenges. Calderón’s inverse conductivity problem is then derived from Maxwell’s equations, and basic properties of the conductivity equation are discussed. Some knowledge of elliptic partial differential equations and Fourier transforms is useful here, but there is a strong effort to make the material as self-contained as possible. Analytic expressions are computed for the Dirichlet-to-Neumann map in case of rotationally symmetric conductivities. This makes it possible to study in concrete terms (i) Alessandrini’s example showing the ill-posedness of EIT, (ii) Calder’on’s original reconstruction approach, and (iii) Ikehata’s enclosure method. The rest of the course is devoted to the use of Complex Geometric Optics solutions for uniqueness proofs and reconstruction via the D-bar method. Recommended to take together with Electrical Impedance Tomography: Computation and Applications
Learning outcomes: Insight into the theory of EIT, including nonlinearity, ill-posedness, and reconstruction approaches.
Prerequisites: Introductory exposure to PDEs and Fourier transforms.

COG1: Fundamentals of Inclusive and Accessible Design of Technology  

Time: : 3.8. - 7.8.2026
Study mode: Hybrid  
Max. number of participants: 30
Lecturers: Markku T. Häkkinen, PhD (Educational Testing Service ETS, USA) and Helen T. Sullivan, PhD (Rider University, USA)  
Coordinator: Laura Mononen
Code: KOGS5750  
Modes of study: Lectures, demonstrations, readings.  
Credits: 3 ECTS  
Evaluation: Pass/Fail. Obligatory attendance at all lectures and lab sessions. Active participation is required. In addition, participants will present a problem in Inclusive Design and a proposed, evidence-based solution in a 7 - 10 minute oral presentation prepared beforehand. Each participant filling the above-stated requirements will receive a diploma of participation to the workshop, but to receive a course diploma with credit statement (3 ECTS) the student must also return a written project report.
Contents: This course bridges the fundamentals of sensory, perceptual, cognitive and physical capabilities with a growing technological toolbox to create devices and services that work for individuals with and without disabilities. This topic becomes even more important with the implementation of the EU Web Accessibility Directive and the European Accessibility Act which came into effect in 2025, and similar requirements in a growing number of countries. To build inclusive and accessible technologies that work for a broad range of human abilities and disabilities requires understanding of how people sense and perceive information, how information design (and complexity) impacts the ability to understand information, and how physical (or virtual) interface design impacts a user’s ability to operate it. Emerging technologies, such as multi-modal generative AI, multi-modal interfaces, sensors, and IoT provide a rich set of tools that can augment, or offer new modes of, interaction with our environment, devices, systems, and services. This course will include lecture, hands on demonstrations, and exercises to understand the challenges and new opportunities for inclusive and accessible design.  
Learning outcomes: Students who successfully complete the course will be able to understand how to apply fundamental principles in inclusive and accessible design to guide creation of new applications or systems, and how they can begin to apply this knowledge in their work and research. Crucial to this is understanding the role guidelines and technical standards play in defining legal requirements for accessibility. Knowledge of these fundamentals will increase the probability of creating highly usable and accessible products for a broad audience, including those with disabilities. Students will also understand the benefits and limitations in using AI to address accessibility.  Motivated students can use successful completion as a basis for further study or research in the field of inclusive design and accessibility.  
Prerequisites: Students should have a background in cognitive science, information systems, computer science or related discipline; or approval of instructors.

COG2: Accessible Visualizations: Conveying Information Across Sensory Modalities Hands-on Lab 

Time: : 10.8. - 14.8.2026
Study mode: In person  
Max. number of participants: 15  
Lecturers: Markku T. Häkkinen, PhD (Educational Testing Service ETS, USA) and Helen T. Sullivan, PhD (Rider University, USA)  
Coordinator: Laura Mononen  
Code: KOGS5751 
Modes of study: Lecture/Lab  
Credits: 3 ECTS  
Evaluation: Pass/Fail. Obligatory attendance at all lab sessions. Active participation is required. In addition, participants may work individually or in small groups. The class will examine the topic of data visualization from the context of accessibility and explore how sensory transformation and adaptation can be used to address specific sensory or cognitive needs. Ideally, the students are expected to bring their own data to be used in this course, whether it is research data to be presented in a poster or presentation, or data representative of their field of work or research. Students will develop an empirically based approach to supporting one or more accessibility solutions and implement a demonstration using a toolbox of technologies made available in the lab. The resulting solution will be presented in the final day by an oral presentation and demonstration. Each participant filling the above-stated requirements will receive a certificate of participation in the course, but to receive a course diploma with credit statement (3 ECTS) the student must also return a project report describing the basis of their approach.
Contents: Data visualization is a key component of how we communicate research findings, real time economic data, or climate measurements, to name a few. In the context of the EU Accessibility Act, and similar legislation in other countries, ensuring the accessibility of data visualization is becoming a requirement.  A key aspect of digital accessibility is ensuring that information can be perceived and understood irrespective of the cognitive and sensory capabilities of the individual, for example, persons with visual, auditory or cognitive disabilities. The principles of accessible design also have relevance for data presentation where environmental or situational factors limit usefulness of what might seem preferred modalities, for example, industrial workers engaged in high workload tasks receiving critical life safety data. By understanding the foundational concepts of our sensory/perceptual systems, and the requirements of accessible design, we will explore how data can be adapted and transformed to suit a variety of individual needs. Modalities examined will include speech and non-speech audio, tactile displays, haptics, and visual adaptation. Students will also learn to effectively utilize multimodal generative AI to support the creation of data visualizations. 
Learning outcomes: Through a combination of lecture and laboratory projects, students will uncover the foundational principles of designing alternative representations for traditionally visual or auditory information. These principles will be aligned with requirements defined by EU and international Accessibility Standards. Students will also understand how attention to accessible design of information can be of broader benefit to users in a variety of contexts.    
Prerequisites: This course is open to graduate students interested in learning how to make their data accessible to the widest possible audience, including for those with disabilities.  Prior coursework in accessibility or cognitive science is welcome but not required.

COG3: Tools for Interaction Design 

Time: 10.8. - 14.8.2026. 
Study mode: In person 
Max. number of participants: 40
Lecturer: Antti Salovaara (Aalto University, Finland)
Coordinator: Laura Mononen
Course code: KOGS5752
Modes of study: In-class exercises, Readings, Individual design exercise (completed in class and as a homework during and after the lecture week)
Credits: 3ECTS  
Evaluation: Pass/fail. The course will be passed if the student submits an accepted mid-term assignment in Tuesday evening and the final assignment on Sunday evening. For both assignments, detailed requirements will be provided. Maximum of one half-day absence is allowed.
Contents: This course introduces participants to the basics of user interface design, mostly for screen-based systems such as desktop and mobile applications. The focus is on methods, theories and concepts that are commonly used in UX/UI profession. After the course, the student has readiness to critically evaluate and discuss the benefits and drawbacks of different design choices on interaction level when they participate in digital product design projects.
Learning outcomes: After successful completion of this course, students will:
•    Be able to sketch and design interaction sequences and user flows, by considering UI design beyond individual screen layouts only;
•    Apply design patterns and other interface concepts in interaction design, informed by theories from psychology and human factors;
•    Be aware of digital tools in interaction design;
•    Carry out a heuristic expert-based usability evaluation on a UI;
•    Has an initial understanding on how graphical design can be integrated to UI design via design systems.

Prerequisites: Students should have a background in cognitive science, information systems, computer science, design or some other discipline that studies humans as users of technology.

COM1: Evolutionary Multi-Objective Optimization 

Time: 10. - 14.8.2026
Study mode:  In person
Max. number of participants: 30
Lecturers:  Dr. Hisao Ishibuchi and Dr. Lie Meng Pang (Southern University of Science and Technology, China)
Coordinators: Michael Emmerich and Bhupinder Saini
Course code: TIES5990
Modes of study: Attendance and exercises
Credits: 2 ECTS
Evaluation: Pass/Fail. The minimum requirement for passing the course is to take part in the daily lectures and exercise sessions.
Contents: In general, real-world optimization problems include multiple objectives. Thus, they can be formulated as multi-objective optimization problems. Those problems do not have a single optimal solution but multiple tradeoff solutions since multiple objectives cannot be simultaneously optimized by a single solution. However, multi-objective optimization problems are usually handled as single-objective optimization problems to find a single solution by focusing only on a main objective or combining multiple objectives into a scalarizing function. In this course, students will learn how to handle multiple objectives to find multiple candidate solutions by considering the tradeoff relation among the objectives.  Emphasis will be given on the evolutionary multi-objective optimization (EMO) approach where a variety of solutions with different tradeoffs are evolved as a population to search for the entire tradeoff front of a multi-objective optimization problem. This course will address the following topics:
- Formulations of single-objective and multi-objective optimization problems with some examples
- Pareto optimality and its relation to the objective space dimensionality
- Scalarizing functions and their contour lines
- Decision maker's role and preference information
- EMO approach, MCDM approach and their hybrid approach
- Basic framework of single-objective evolutionary algorithms
- Basic framework of multi-objective evolutionary algorithms 
- Search behavior analysis of NSGA-II and its modifications
- Related websites: PlatEMO and Pymoo 
- Search behavior analysis of MOEA/D and its modifications
- Search behavior analysis of SMS-EMOA and its modifications
- Search behavior analysis of NSGA-III and its modifications
- Difficulties in performance comparison of EMO algorithms
- Performance indicators: Uniformity, s-energy, GD, IGD, IGD+ and HV
- Anytime performance analysis
- Population size specification for performance comparison
- Artificial test problems and real-world problems
- Performance improvement of EMO algorithms: Archiving and initialization
- Constraint handling in EMO algorithms
- Special multi-objective problems: Many-objective, large-scale, and sparse problems
- Use of machine learning techniques for EMO algorithms
- Use of large language models for EMO research

Learning outcomes: After completing the course, students will have clear ideas about evolutionary algorithms and evolutionary multi-objective optimizations. They will be familiar with basic concepts in multi-objective optimization such as Pareto dominance and Pareto fronts, representative multi-objective evolutionary algorithms such as NSGA-II, MOEA/D and SMS-EMOA, performance indicators such as GD, IGD and hypervolume, and some hot topics such as archiving and Pareto set learning. They will also understand the importance of fair performance comparison.
Prerequisites: Participants are expected to have prior knowledge of the following concepts:
- Basics of probability theory

COM2: Portable GPU Programming

Time: 11. - 14.8.2026 (NB! The course lasts 4 days, Tue-Fri)
Study mode: In person 
Max. number of participants: 30 
Lecturers: Dr. Tuomas Rossi (CSC – IT Center for Science, Finland) and Dr. Jussi Enkovaara (CSC – IT Center for Science, Finland)
Coordinator: Prof. Tuomo Rossi
Course code: TIES3440
Modes of study: Lectures, demonstrations, and hands-on exercises.
Credits:  1 ECTS 
Evaluation: Pass/Fail based on attendance
Contents: This course provides a practical, hands‑on introduction to portable GPU programming. Participants will learn how to develop hardware‑agnostic, high-performance applications for diverse accelerated computing environments using OpenMP offload and Kokkos as example frameworks. The course combines lectures with hands-on exercises on the LUMI and Roihu supercomputers, enabling participants to work with both AMD and NVIDIA GPUs to explore cross‑platform portability.

Learning outcomes: After completing the course, participants should be able to

• Explain the key architectural features of modern GPUs and their implications for performance
• Develop hardware-agnostic accelerated applications using OpenMP offload and/or Kokkos to express parallelism
• Implement effective memory management strategies across host and accelerator environments
• Compare and critically assess different GPU programming models in terms of portability, performance, and ease of use
  Prerequisites: Basic skills to operate in a Linux command line environment.
• Basic working knowledge in programming with C or C++. Necessary C++ constructs will be introduced to those familiar with C only. The OpenMP section may also be completed using Fortran instead of C or C++.
• Prior exposure to scientific computing concepts is helpful but not required.
• Participants should bring their own laptop for accessing the supercomputers where hands‑on exercises are conducted. Any laptop capable of SSH access and web browsing is sufficient.

CYB1: Cyber Security – Management and Technology

Time: 10. - 14.8.2026
Study mode: In person
Max. number of participants: 40, of which at least 10 from outside the organizing universities
Lecturers: Martti Lehto (JYU), Dominic Saari (JYU), Kimmo Halunen (OY), Marko Helenius (TAMU), Alina Torbunova (ÅA), Emmanuel Anti (UWASA) 
Coordinators: Martti Lehto and Piia Perälä
Course code: KYBS4650
Modes of study: Obligatory attendance at lectures and completing the exercises
Credits:  3 ECTS
Evaluation: Pass/fail     
Contents: The summer course is organized in cooperation with several Finnish universities. The course will enhance understanding of rapidly changing cyber security environment. During the course, the students will get familiar with cyber security phenomena and elements. Through lectures and workshop case studies, the students will learn to identify vulnerabilities, threats and how to build cyber resilience. Topics covered include, for example, critical infrastructure protection, artificial intelligence and cybersecurity, cyber security management, cyber warfare and software security. The course will be interactive, encouraging the students in critical thinking concerning cyber security building. The lectures and workshops are produced by several visiting lecturers from organizing universities.
Learning outcomes: Basic common understanding about Cyber Security.
Prerequisites: Candidate level degree in Computer Science, Information technology, or comparable sufficient technological expertise, sufficient knowledge of programming.

QST1: Non-classicality in Quantum Theory and Beyond

Time: 10. - 14.8.2026
Study mode: In person
Maximum number of participants: 30
Lecturer: Leevi Leppäjärvi (JYU)
Coordinator: Hanwool Lee
Course code: TIEJ6005
Modes of study: Lectures + reading assignments + exercises 
Credits: 2 ECTS
Evaluation: Pass/Fail
Contents: By looking at quantum theory from the perspective of a more abstract operational framework one is able to study its properties in a wider context. This allows us to identify some of the physical features characteristic of quantum theory and it helps us to understand what makes quantum theory special among other possible theories. From the information-theoretic point of view this gives us insight into the resources and the advantages of quantum information processing over its classical counterpart. In this course we present the convex-geometric formulation of quantum theory, generalize it to include other convex operational theories and examine various non-classical features of quantum theory such as non-locality and measurement uncertainty in this framework.
Learning outcomes: 
1. basic understanding of the extent of non-classicality in quantum theory,
2. basic understanding of the convex-geometric tools needed to describe quantum theory and other operational theories
Prerequisites: Analysis, linear algebra, basics of quantum theory

QST2: Quantum Cellular Automata

Time: 3. - 7.8.2026
Study mode: In person
Maximum number of participants: -
Lecturer: Prof. Paolo Perinotti (Univ. Pavia, Italy) 
Coordinator: Teiko Heinosaari
Course code: TIES5700
Modes of study: Lectures, exercises
Credits: 1-2 ECTS (1 ECTS for completing exercises during the course +1 ECTS for a written assignment, return a few weeks after the course)
Evaluation: Pass/Fail
Contents: The theory of quantum cellular automata can be traced back to 1982, with Feynman’s famous paper that—along with David Deutsch’s paper on the quantum Turing machine—gave birth to the idea of quantum computing.   It is only in 2004, however, that a thorough algebraic theory was developed by Schumacher and Werner. In the course we will introduce the latter formulation of the notion of a quantum cellular automaton, and use it to study various aspects of the theory. We will focus in particular on the problems of classification and renormalization.   
Learning outcomes: Basic knowledge of the theory of quantum cellular automata and quantum walks
Prerequisites: Linear algebra, basic quantum theory  

ISS1: Entrepreneurship Opportunities in Blockchain Technology

Time: 3. - 7.8.2026
Study mode: Hybrid
Maximum number of participants: -
Lecturer: Adjunct Prof. Dandison Ukpabi (Visiting Researcher, University of Eastern Finland & Researcher, University of Jyväskylä) 
Coordinator: Christian Igbegeh
Course code: TIES3600
Modes of study: Lectures, demonstrations, exercises, group work 
Credits: 2 ECTS
Evaluation: Pass/fail 
Contents: Entrepreneurship Opportunities In Blockchain Technology intersects IT and business disciplines. It immerses students in the fast-evolving world of blockchain from an 
entrepreneurship perspective. Through market insights, real-world case studies, and hands-on exposure to essential Web3 tools, participants learn how to identify high-value opportunities, 
design viable blockchain-driven business models, and confidently transform bold ideas into 
scalable ventures.
Learning outcomes: Students will be able to: 
1. Understand Blockchain’s Business Value: Gain a strong grasp of how blockchain 
creates economic advantage, transforms industries, and opens new entrepreneurial 
pathways across global markets. 
2. Identify High-Potential Market Opportunities: Learn to analyze blockchain trends, 
evaluate market gaps, and pinpoint viable, scalable business opportunities with real 
commercial demand. 
3. Design Viable Blockchain Business Models: Develop the ability to craft sustainable 
value propositions, revenue models, and token-driven ecosystems tailored to 
emerging Web3 markets. 
4. Apply Essential Blockchain Tools Strategically: Build confidence using key Web3 
tools—wallets, dApps, analytics, and tokenomics frameworks—to support informed 
business decision-making. 
5. Develop and Pitch Blockchain Venture Ideas: Transform insights into actionable 
startup concepts and deliver compelling pitches supported by market analysis, 
validation, and strategic planning. 
Prerequisites: First degree in any discipline