Summer School

The Summer School will be more than just taking a course

Jyväskylä Summer School is a great opportunity to earn credits in the summer, learn more about interesting topics and enjoy studying in an international atmosphere. The Summer School also allows the students to widden their knowledge to outside their own field so it's highly recommended to go through all Summer School courses. All courses are taught in English. You can find the courses for Jyväskylä summer school below, but please be aware that the course information is still tentative and subject to change. The information will be updated on this website as further details become available. The Summer School also allows the students to widden their knowledge to outside their own field so it's highly recommended to go through all Summer School courses. A total of 20 courses are offered in the following subjects:

NANO1/BIO1/CH4: Where are the protons? Measuring and modelling proton equilibria in complex macromolecular systems

Time: 7.-11.8.2017, 16h lectures + 11h practicals (includes 3h for students poster presentations) +  3h lecture/panel discussion
Place: Lectures: YAB310 ,demonstrations: MaD 205, labratory work: YAB322
 no limits
Lecturers: Dr. Gerrit Groenhof (University of Jyväskylä, Finland), Dr. Serena Donnini (University of Jyväskylä), Prof. Janne Ihalainen (University of Jyväskylä), Prof. Matthias Ullmann (University of Bayreuth, Germany), Prof. Sebastian Westenhoff, (University of Gothenburg, Göteborg, Sweden) and Associate professor Frans Mulder (University of Aarhus, Denmark).
Coordinator: Dr. Serena Donnini (University of Jyväskylä)
Code: SMBV101
Passing: Obligatory attendance at lectures, and completing the practicals.
Credits: 3 ECTS
Grading: Pass/Failed
Prerequisites: No prerequisite. Previous courses in chemical physics, physical chemistry, or biological chemistry are appreciated.
Abstract: Nanoscience is a cross-disciplinary research area which combines physics, chemistry, and biological and environmental science. Various different molecular nanosystems are in the focus of the research. The protonation state of each ionisable group of a molecule influences, mainly via its charge, the physicochemical properties, the structure and the function of the molecule. Determination of the protonation states in complex macromolecular systems is not an easy task, as it depends on the electrostatic environment of a ionisable group, and on the solution pH. During the summer school, most recent experimental and computational approaches to identify protonation states in proteins are discussed, and hand-on practicals provided. In particular, nuclear mass resonance and infrared spectroscopy for measuring proton equilibria will be described, together with computational methods, such as molecular dynamics simulations at constant pH and Poisson-Boltzmann methods for predicting protonation states. The last day of the course, we will have a poster session and a round table to discuss challenges and more recent advances inherent to these methods.


NANO2/PH5/CH5: Materials for Nanophotonic Applications

Time: 14.–18.8.2017, 12 h lectures and 4 h exercises
Place: YN 341
Participants: no limits
Lecturers: Prof. Dr. Thomas Fuhrmann-Lieker (University of Kassel / CINSaT, Germany), Prof. Dr. Thomas Kusserow, (INA, University of Kassel / CINSaT, Germany)
Coordinators: Dr. Jussi ToppariProf. Mika Pettersson (University of Jyväskylä)
Passing: Obligatory attendance at lectures, and completing the exercises
Credits: 2 ECTS
Grading: Pass/fail
Prerequisites: The course is targeted at students with a background in electrodynamics and solid-state physics. Basic knowledge in chemistry and optics will be helpful, but the most important concepts are introduced in the lecture as well.
 Nanoscience is a cross-disciplinary research area which combines chemistry, physics, and biological and environmental science. Various different molecular nanosystems are in the focus of the research.
Fundamental concepts and properties of inorganic and organic materials for optical applications will be presented. The interaction of light with matter on the microscopic scale is giving a good description of the basic effects, but has to be linked to the macroscopic effects that we can observe and apply for optical devices. A special focus of our lecture will be on optical application in the nanometer range, enabling the use of efficient superposition effects, material averaging and near-field optics. The lecture will be complemented by various examples for applications and an introduction to the optical characterization of materials.

CH1: Bioinorganic Chemistry

Time: 7.–11.8.2017, 20 h lectures + home exam
Place: KEM 2 (lectures)
Participants: max 40
Lecturer: Prof. Evamarie Hey-Hawkins (Leipzig University, Germany)
Coordinator: Prof. Matti Haukka (University of Jyväskylä)
Code: KEMV114
Passing: Obligatory attendance at lectures, and completing the home exam
Credits: 2 ECTS
Grading: Pass/fail
Prerequisites: B.Sc. in Chemistry, Biochemistry, or Biology
Abstract: Metalloenzymes: bioelements, bioligands, physical methods. O2 transport and activation. Iron: uptake, transport, storage, iron proteins. Copper proteins. Cobalamines. ”Early” transition metals: Mo, nitrogen fixation. Nickel: urease. Zink.


CH2: Towards circular economy of metals - using hydrometallurgy

Time: 14.–18.8.2017, 12 h lectures + home exam
Place: KEM 2 (lectures)
Participants: no limits
Lecturer: Assistant Prof. Mari Lundström (Aalto University, Finland)
Coordinator: Prof. Heikki Tuononen (University of Jyväskylä)
Code: KEMV113
Passing: Obligatory attendance at lectures, and completing the home exam
Credits: 2 ECTS
Grading: Pass/fail
Prerequisites: B.Sc. in Chemistry, Physics, or Technology
Abstract: Metals have been used by man since ancient ages. However, many of the existing ore bodies are becoming of low grade. At the same time, increasingly complex metal, metal alloy and composite containing products must be recycled. Hydrometallurgy is an essential tool in the production of metals both from primary raw materials (ores/concentrates) as well as from the secondary raw materials (e.g. waste electronic and electrical equipment). This course provides the basics of the metals circular economy, focusing on hydrometallurgical metal production and unit processes. During the course the students will familiarize themselves to the metal production in Finland. Also underutilized metal containing side streams and waste materials will be discussed. Primary and secondary production of copper will be addressed specifically. Hydrometallurgical processing of metals, general hydrometallurgical flow sheet and hydrometallurgical unit processes related to leaching, solution purification, product recovery including electrowinning and electrorefining, reaction thermodynamics and kinetics will be in focus. The course content includes some topics related to process design such as typical process design documents. The main themes of the include topic such as (not a complete list): metallurgy and circular economy, leaching, thermodynamics and kinetics, solution purification and product recovery and process design.

CH3: State-of-the-art of Fluorescent Probes: Chemistry for Molecular Biology and Medicine

Time: 7.-11.8.2017, 16 h lectures + exam
Place: KEM 2
Participants: no restrictions
Lecturer: Prof. Vladimír Král (Biotechnology and Biomedicine Center of the Academy of Sciences and Charles University in Vestec, Prague, Czech Republic)
Coordinator: Docent Elina Sievänen (University of Jyväskylä)
Code: KEMV115
Passing: Obligatory attendance at lectures, homework
Credits: 2 ECTS
Grading: Pass/fail
Prerequisites: B.Sc. in Chemistry, Biochemistry, or Biology
Abstract: In the first part of the course synthesis of currently used fluorophores and characterization techniques of their photophysical properties, chemical stability, and photostability (photobleaching) are described. Novel molecular structures that can be excited by simultaneous absorption of two photons, thus providing three-dimensional resolution, will be introduced as well. In the second part of the course applications will be presented. For example, the use of fluorescence microscopy (wide field, confocal, multiphoton, and most recently superresolution) in combination with fluorescence probes comprises a powerful set of scientific tools to study live cells. The fundamental properties of synthetic and genetically encoded fluorescence indicators used for cellular morphology imaging and signaling recording are covered and current state-of-the-art for imaging and bioimaging summarized. Finally, applications of some of the discussed methodologies, including photocatalytic degradation of pollutants, steroid hormones, and polyaromates, are discussed.


COM1: Fast Boundary Element Methods 

Time: 7.–11.8.2017, 24 h lectures and 16 h demonstrations
Place: Lectures: Ag C232, demostrations:  Ag B112.2 (Latin)
Lecturer: Prof. Dr.-Ing. Stefan Kurz (Robert Bosch GmbH/TU Darmstadt, Germany), Instructor: M.Sc. Felix Wolf (TU Darmstadt, Germany)
Coordinator: Dr. Sanna Mönkölä (University of Jyväskylä)
Code: TIES675
Lectures: 24 hours and 16 hours demonstrations/exercises
Credits: 3-4 ECTS 
Maximum number of students: no limits
Passing: Obligatory attendance at lectures and completing the exercises. 
Grading: Pass/fail
Prerequisites: Basics of numerical methods for partial different equations (e.g., finite difference or finite element method), vector calculus, linear algebra, and some programming experience. 
Abstract: How to solve field problems numerically on the computer? The Boundary Element Method (BEM) has developed into an important alternative to domain-oriented approaches (like Finite Elements),ever since fast implementations are available. The BEM reduces the dimensionality of the problem and can easily take into account unbounded domains.
Starting from the representation formulas of Kirchhoff and Stratton-Chu boundary integral equations are derived. Next, their discretization by collocation and Galerkin methods is discussed.
The resulting fully populated matrices have to be compressed for practical applications, by Fast Multipole or Adaptive Cross Approximation methods.
Industrial examples for application of the BEM are considered, for instance acoustic and electromagnetic scattering problems,and thermal analysis.
Programming homework will be assigned, to deepen the students’ understanding of the contents.

COM2: Baire's theorem and some of its consequences

Time: 7.–10.8.2017, 8 h lectures and 8 h exercises
Place: Ag C233
Participants: no limits
Lecturer: Dr. Marcus Waurick (University of Bath, UK)
Coordinator: Dr. Sanna Mönkölä (University of Jyväskylä)
Code: TIES676
Passing: Obligatory attendance at lectures and completing the exercises.
Credits: 3 ECTS
Grading: Pass/fail
Prerequisites: Basic knowledge of Banach spaces
Abstract: In this course, we discuss Baire's category theorem, that is, roughly speaking, a complete metric space is thick in a certain sense. We provide corollaries thereof, such as the closed graph theorem or the open mapping theorem. We will further prove that the algebraic dimension of an infinite dimensional Banach space is necessarily uncountable. The results have also implications for numerical analysis.


COM3: Data-driven optimization via search heuristics

Time: 14.–18.8.2017, 15 h lectures + 15 h demonstrations
Place: Lectures: Ag Beeta, demonstrations: Ag B112.2 (Latin)
Participants: no limits
Lecturer: Dr. Richard Allmendinger (University of Manchester, UK)
Coordinator: Dr. Jussi Hakanen (University of Jyväskylä)
Code: TIES677
Passing: Obligatory attendance at lectures. The course will comprise lectures and group-based work to prepare a small-group presentation summarizing the application of optimization to a real-world problem.
Credits: 4 ECTS
Grading: Pass/fail
Prerequisites: The course will build on basic concepts in probability, statistics, and discrete mathematics. It would be suitable for anyone with a numerate background who has an interest in learning about optimization and/or machine learning. Programming experience is beneficial but not a pre-requisite.
Abstract: The course covers the emerging topic around data-driven optimization, which deals with problems that vary from the default formal problem description consisting of equations and functions. We will discuss a range of examples of data-driven problems covering both problems relying on simulations and/or physical experiments in the evaluation of solutions. The application of search heuristics, such as evolutionary algorithms, has become crucial in this domain. This course will introduce students to the core search heuristics and real-world challenges that need to be accounted for when tackling data-driven optimization problems. The following topics will be covered in the course:

  • Fundamentals of optimization, decision making, and search heuristics
  • Simulation meets optimization
  • Experimental and expensive optimization
  • Using data only to conceptualize and optimize a problem
  • Uncertainty and constraint-handling
  • Multiobjective and mixed-integer optimization
  • Data-driven real-world applications of search heuristics
  • Drug discovery
  • Design of manufacturing processes
  • Instrument setup tuning
  • Portfolio optimization
  • Allocation of computational resources
  • Production planning

COM4: Numerical Methods for Finance

Time: 14.–18.8.2017, 20 h lectures + demos
Place: Lectures: Ag C231, demostrations: MaD205
Participants: no limits
Lecturer: Prof. Jari Toivanen (University of Jyväskylä, Finland & Stanford University, USA)
Coordinator: Dr. Sanna Mönkölä (University of Jyväskylä)
Code: TIES678
Passing: Obligatory attendance at lectures and completing the exercises
Credits: 3 ECTS
Grading: Pass/fail
Prerequisites: Basic calculus and linear algebra, some familiarity with finite difference methods.
Abstract: In financial markets, many different kinds of assets are available.
For investment purposes the most important ones are stocks and interest bearing instruments. The basic models for these are described. For example, a geometrical Brownian motion is a common model for stocks. The Monte Carlo method based on simulating paths for these and portfolios combining these is considered. Also simple portfolio analyses and optimizations based on these simulations are considered.
Vanilla options giving the right to sell (put) or buy (call) a given underlying asset like stock at its expiry. The Monte Carlo method is also considered for pricing options. Another approach is to formulate a partial differential equation (PDE) for the option price. A famous example is the Black-Scholes PDE. A basic finite difference method is described for solving resulting PDEs.


IS1: Online Social Media Analytics

Time: 7.-11.8.2017, 10 h lectures + 5 h demonstration
Place: Lectures: Ag D211, demostrations: Ag B112.1 (Africa)
Participants: max 45
Lecturer: Assistant Professor Alexander Nikolaev (University at Buffalo, United States)
Coordinators: Prof. Jari Veijalainen and Dr. Alexander Semenov (University of Jyväskylä)
Passing: Obligatory attendance on lectures, and completing exercises.
Credits: 3 ECTS
Grading: 1 to 5
Prerequisites: The students will be expected to work with mathematical models and analytical reasoning. Basic knowledge of matrix algebra, statistical analysis, and probability theory is required.
Programming experience (in some language) is strongly encouraged. Knowledge of stochastic processes and optimization techniques is encouraged but not required.
Abstract: Online social media analytics is an emerging field in modern science, thanks to the growing abundance of data. Seeking to enhance our understanding of the principles and patterns of the information exchange and opinion formation in the society, this course is intended to review key concepts involved in the analysis of online user contributed content. It will rely on the scholarship in data analysis and mining, with the purpose of taking an in-depth look at theories, methods, and tools to examine the content, structure and dynamics of social media. The course offers an introduction to the key theoretical concepts in text and social network analytics, and primarily aims at supporting future applied investigations of interest to the audience, through hands-on practice tutorials.

IS2: Optimization Approaches to Analyzing Robustness of Complex Networks

Time: 14.-18.8.2017, 20 h lectures + 10 h demonstration
Place: Lectures: Ag C233, demostrations: Ag B212.1 (Finland)
Participants: max 45
Lecturers: Associate Professor Vladimir Boginski, (University of Central Florida, USA), Associate Professor Oleg Prokopyev (University of Pittsburgh, USA), Research Assistant Scientist Alexander Veremyev (University of Florida, USA)
Coordinator: Dr. Alexander Semenov (University of Jyväskylä)
Code: TJTS584
Passing: Obligatory attendance on lectures, and completing exercises.
Credits: 3 ECTS
Grading: Pass/fail. Final grade (pass/fail) will be based on homework assignments and course project (analysis of a real-world network using techniques learned in class). 
Prerequisites: Basic knowledge of matrix algebra, calculus, statistical analysis, and probability theory is required. Programming experience (in some language) is strongly encouraged. Knowledge of optimization techniques, as well as experience with Gurobi or another optimization solver software, is encouraged but not required.
Abstract: Complex networks arise in a variety of domain areas, including information exchange networks, electric power grids, transportation networks, social networks, biological networks, and many others. One of the important areas of research is to ensure robustness in these networked systems, where various parameters and metrics can be considered in order to quantify robustness and vulnerability to node and/or link disruptions. In this course we will discuss some aspects of this broad area, including identification and design of "highly connected" robust clusters in complex networks, as well as finding "critical elements" of a network that are important for preserving its connectivity. Emphasis will be put on optimization-based approaches, such as integer programming techniques. Modern optimization software packages (Gurobi, FICO Xpress, CPLEX, etc.) have been demonstrating significant performance enhancements over the last decade. These improvements allow finding solutions for many combinatorial problems on real-life graphs via efficient mixed integer programming (MIP) formulations. Moreover, exact, approximation, and heuristic algorithms can be developed for these problems. In this course, we will present a variety of interesting problems arising in this area and familiarize the students with theoretical, computational, and application-based aspects of analyzing robustness of real-world complex networks.


KOG1: Accessible and Inclusive Design of ICT: Foundational Introduction to Human Sensory, Cognitive, and Physical Limitations and Technological Solutions

Time: 7.-11.8.2017, 20 h lectures + lab sessions
Place: Ag C234
Participants: max 25
Lecturers: Dr. Markku Hakkinen (Educational Testing Service, United States), Assistant Professor Helen Sullivan (Rider University, United States), and a number of other international experts as participants of the online panel discussion day.
Coordinator: Rebekah Rousi
Code: KOGS581
Passing: Obligatory attendance at all lectures, lab sessions, and field visit to local school for students with disabilities. Active participation is required. In addition, participants will present a challenge areas in accessibility and their proposed solution in the form of a poster, demo, or 5 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 (2 ECTS) the student must also return a 1-2 page report (evaluate what you have learned and how can/will you use it in your future research).
Credits: 2 ECTS  
Grading: Pass/fail
Prerequisites: Should have a background in information systems, computer science or related discipline.  
Abstract: The field of accessibility has grown over the past 25 years from a niche specialty to one with increased focus and demand across multiple industries.  Whether building innovative mobile apps, creating engaging games for learning or entertainment, developing social media web sites that reach millions, publishing scientific information or learning materials, or designing innovative new consumer technologies, ignoring accessibility can limits markets and exclude people from using your product or service.  As an emerging area that blends science and technology to solve challenging problems in how people can use technology, its importance is bolstered by increasing national legislation requiring that technology be usable by individuals who have disabilities.
This class will address the range of issues that one must be aware of to meet that challenge and will cover how to make technology work for people with sensory, cognitive, or physical impairments, a population (and market) that comprises more than 1 billion people globally who have one or more disabilities.
To solve the challenge 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.  Sensory substitution or adaptation is a key concept, but one that introduces challenges involving perceptual bandwidth, information equivalence and cognitive load.  Utilizing multiple modalities can address some of these challenges, as can mapping semantic structures inherent in one modality to the navigation and presentation of information in another. Emerging technology platforms, such as cloud-based cognitive computing, may offer opportunities to enable sensory transformation through means such as auditory or image classification.
Accessibility technical standards and guidelines, notably from the World Wide Web Consortium (W3C), can be applied in the development of software applications and systems. These same standards have been incorporated into the national legislation in the USA, Europe, and a growing number of countries. Understanding these standards and how to apply them can pose challenges, particularly with emerging technologies. The course will introduce students to the key standards, how to apply them, and how to evaluate whether an application or system is in conformance.
The course will introduce students to the fields of accessibility, characteristics and demographics of disability, foundational understanding of sensations & perception, issues in sensory substitution and transformation, multimodal approaches, interfaces design and cognitive load, technical standards, legal requirements, and implementing accessibility through a series of demonstrations and labs.  Students will be presented with a pool of accessibility challenges from which they may choose one as the basis of a short project/paper/presentation. Alternately, students may choose a challenge identified from their own research discipline or work.
Students who successfully complete the course will be able to understand the importance of accessibility, how accessibility standards and legislation can guide design of new applications or systems, and how they can begin to apply this knowledge in their work and research. Knowledge of these fundamentals will increase the probability of creating highly usable and accessible products.  Given the introductory nature of this course, motivated students can use successful completion as a basis for further study or research in the field of accessibility.

MA1: Optimal Mass Transportation and Geometric Inequalities

Time: 7.–11.8.2017, 10 h lectures
Place: MaD 259 (lectures Mon-Fri 14:00-16:00)
Participants: no limits
Lecturer: Prof. Dr. Zoltán Balogh (Universität Bern, Switzerland)
Coordinator: Dr. Tapio Rajala (University of Jyväskylä)
Code: MATS536 
Passing: Obligatory attendance on lectures, and completing exercises.
Credits: 2 ECTS
Grading: Pass/fail
Prerequisites: Basic measure theory
Abstract: Optimal mass transportation is a powerful tool to prove geometric inequalities in various settings of metric measure spaces. In this series of lectures I will indicate such applications in the setting of Euclidean, Riemannian and sub-Riemannian spaces. As a consequence we obtain Brunn-Minkowski, Borell-Brascamp-Lieb and entropy inequalities.  


MA2: Stochastic calculus of variations and normal approximations

Time: 7.–11.8.2017, 10 h lectures
Place: MaD 259 (lectures Mon-Fri at 9:00-11:00)
Participants: no limits
Lecturer: Prof. David Nualart (The University of Kansas, USA)
Coordinator: Prof. Stefan Geiss (University of Jyväskylä)
Code: MATS537
Passing: Obligatory attendance on lectures, and completing exercises.
Credits: 2 ECTS
Grading: Pass/fail
Prerequisites: probability based on measure theory, elements of stochastic processes
Abstract: The goal of this course is to give a brief introduction to the Malliavin calculus and discuss its applications to normal approximations. The Malliavin calculus is an extension of the calculus of variations from deterministic functions to stochastic processes, that was introduced by Paul Malliavin to provide a probabilistic proof of Hörmander’s hypoellipticity theorem. In the first part of the course we will introduce the basic facts of the theory of stochastic processes, including the construction and main properties of Brownian motion. We will define the Itô stochastic integral with respect to the Brownian motion and show the corresponding change of variable formula. In the second part, we  will introduce the  derivative operator and  the divergence operators on the Wiener space and we will show that the divergence is an extension of the Itô integral to anticipative processes. We will also define multiple stochastic integrals which lead to  the Wiener chaos expansion and we will show how the derivative and divergence operators act on the chaos expansion. We will introduce the Ornstein-Uhlenbeck semigroup and study its generator. In the last part of the course we will present the application of the Malliavin calculus, combined with Stein’s method, to derive rates of convergence in normal approximations.


MA3: Lectures on optimal entropic transport

Time: 14.–18.8.2017, 10 h lectures
Place: MaD259
Participants: no limits
Lecturer: Prof. Christian Leonárd (Université Paris Ouest, France)
Coordinator: Dr. Tapio Rajala (University of Jyväskylä)
Code: MATS538
Passing: Obligatory attendance on lectures, and completing exercises.
Credits: 2 ECTS
Grading: Pass/fail
Prerequisites: Basic probability theory including the notion of conditional expectation (disintegration of measures). Some acquaintance with Brownian motion and convex duality could be helpful, but are not mandatory. They will be introduced in a nutshell.
Abstract: An optimal transport problem consists of finding an interpolation between two probability distributions minimizing some average cost. Displacement interpolations resulting from standard optimal transport are basic tools (i) for deriving several concentration and geometric inequalities, and (ii) for developing the Lott-Sturm-Villani (LST) theory of curvature lower bounds of geodesic spaces.
Entropic optimal transport is a probabilistic version of the standard transport problem. It consists of finding most probable interpolations between two prescribed configurations described probability distributions, when one travels  randomly, typically along  Brownian diffusion processes on manifolds or  random walks on graphs. In its original form, this optimization problem was addressed by Schrödinger in 1931 as a large deviation problem (the so-called Schrödinger problem) for a random particle system. Its solutions are called entropic interpolations.
We shall introduce both standard and entropic optimal transport problems, their interpretations in terms of large deviations of particle systems and their equations of motion. Some geometric and concentration inequalities will be proved by means of entropic interpolations. Otto's heuristic interpretation of the heat equation as the gradient flow of the entropy with respect to a transport distance will be investigated in light of Schrödinger's problem. Finally, the convergence of entropic interpolations to displacement interpolations on graphs will be investigated.


PH1: Inflationary Cosmology and Primordial Perturbations

Time: 7.-11.8.2017, 10 h lectures and 6 h exercises
Place: FYS2 (lectures Mon-Fri at 10:00-12:00)
Credits: 2 ECTS
Code: FYSV441
Passing: Obligatory attendance at lectures, and completing the exercises.
Grading: Pass/fail
Prerequisites: BSc in physics or equivalent. Some familiarity with the theory of general relativity would be helpful.
Lecturer: Dr. Mindaugas Karciauskas (University of Jyväskylä, Finland)
Coordinators: Mindaugas Karciauskas, Prof. Kimmo Kainulainen (University of Jyväskylä)
Abstract: Cosmological inflation is the preferred model to explain the origin of structure in the Universe, such as galaxies and galaxy clusters. It also explains the initial conditions for the Hot Big Bang. In this course we will review the motivation for cosmological inflation and discuss its main features. We will study in more detail the origin and evolution of cosmological perturbations: from their birth as quantum fluctuations to the late time evolution as a classical perturbation. δN formalism is a very powerful tool to compute the statistical properties of those perturbations. The latter allows confronting the predictions of models of inflation with observations. We will derive the necessary equations of the formalism and apply them to some examples. Multifield models and the so called isocurvature perturbation will also be discussed in the course.


PH2: Gamma-ray spectroscopy

Time: 7.–11.8.2017, 10 h lectures and 3 h exercises
Place: FYS2
Participants: no limits
Lecturer: Prof. Rolf-Dietmar Herzberg (University of Liverpool, UK)
Coordinator: Prof. Paul Greenlees (University of Jyväskylä, Finland)
Code: FYSV442
Passing: Obligatory attendance at lectures, and completing the exercises
Credits: 2 ECTS
Grading: Pass/fail
Prerequisites: BSc in physics or equivalent. Basic knowledge of nuclear physics and interaction of radiation with matter required.
Abstract: The nucleus is a complex many-body quantum system with a large number of excited states. Experimental access to the level scheme, and therefore an understanding of the collective interplay of all nucleons, is through the observation of gamma rays emitted when the nucleus transitions between excited states.  In this course we will cover the basics of high-resolution gamma ray spectroscopy, beginning from an understanding of a Compton-suppressed HPGe detector to the analysis techniques in a multi-detector spectrometer, such as JUROGAM or AGATA. We cover techniques for determining the multipolarity and electromagnetic character of the observed gamma rays, and how to make use of coincidence techniques to build complex level schemes. The course also covers the competing process of internal conversion, where electrons are emitted instead of gamma rays. Participants will gain an understanding of the most commonly found features in nuclear level schemes, such as rotational bands and vibrational structures as well as an appreciation of the challenges typically encountered when analyzing data.
The lectures will be rounded off in two practical sessions, where some of these concepts will be illustrated in a hands-on setting. 

PH3: Transition probabilities as a probe for nuclear structure

Time: 14.–18.8.2017, 6 h lectures and 2 h exercises
Place: FYS 2
Participants: no limits
Lecturer: Dr. Tuomas Grahn (University of Jyväskylä, Finland)
Coordinator: Dr. Tuomas Grahn
Code: FYSV444
Passing: Obligatory attendance at lectures, and completing the exercises
Credits: 1 ECTS
Grading: Pass/fail
Prerequisites: BSc in physics or equivalent. Basic knowledge of nuclear physics and interaction of radiation with matter required. The course PH2 is strongly recommended for all students taking PH3.
Abstract: By definition, electromagnetic transition probabilities between two states in an atomic nucleus involve a matrix element connecting the respective wave functions. Since the electromagnetic interaction is precisely known, one can extract information on the wave functions of the states through measurements of transition probabilities. In this course, the connection between the quantum mechanical wave functions and experimental observables is established. Two different experimental methods that are used to extract transition probabilities are introduced, mean lifetime measurements and Coulomb excitation. These two methods are complementary and in the era of re-accelerated radioactive beams have become standard tools to extract nuclear structure information. After completing the course a student understand the connection between the observables (electromagnetic transition probabilities) and nuclear structure. The student knows the principles of the mean lifetime measurements of excited nuclear states using the stable heavy-ion beams. In addition, the student is familiar with Coulomb excitation measurements done with radioactive ion beams (in inverse kinematics). 


PH4: Nuclear properties and the astrophysical r process

Time: 14.–18.8.2017, 10 h lectures and 6 h exercises
Place: FYS2
Participants: no limits
Lecturer: Associate Prof. Rebecca Surman (University of Notre Dame, USA)
Coordinator: Dr. Anu Kankainen (University of Jyväskylä)
Passing: Obligatory attendance at lectures, and completing the exercises
Credits: 2 ECTS
Grading: Pass/fail
Prerequisites: BSc in physics or equivalent. Basic knowledge of nuclear physics required. 
Abstract: The astrophysical rapid neutron capture process, or r process, of nucleosynthesis is believed to be responsible for the production of approximately half the heavy element abundances found in nature. Though we understand the basics of how the r-process proceeds, its astrophysical site is still not conclusively known. The nuclear network simulations we use to test potential astrophysical scenarios require nuclear physics data (masses, beta decay lifetimes, neutron capture rates, fission probabilities) for all of the nuclei on the neutron-rich side of the nuclear chart, from the valley of stability to the neutron drip line. This course will include a general overview of the r process and discussions of each of the types of nuclear data crucial for r-process calculations.


STAT1: Value of information analysis in spatial models

Time: 14.–18.8.2017, 15 h lectures and exercises in forms of computer projects
Place: Ag B212.1 (Finland)

no limits
Lecturer: Prof. Jo Eidsvik (NTNU, Trondheim, Norway)
Coordinator: Prof. Juha Karvanen (University of Jyväskylä)
Code: TILS970
Passing: Obligatory attendance at lectures and completion of project work using Matlab or R (or other software).
Credits: 2 ECTS
Grading: Pass/fail
Prerequisites: Calculus, probability and statistics, basic computer programming. (Although it is not essential, it helps to know about multivariate analysis and optimization.)
Abstract: We constantly use information to make decisions about utilizing and managing resources. For natural resources, there are often complex decision situations and variable interactions, involving spatial (or spatio-temporal) modeling. How can we quantitatively analyze and evaluate different information sources in this context? What is the value of data and how much data is enough?
The course covers multidisciplinary concepts required for conducting value of information analysis in multivariate and spatial models.
Participants will gain an understanding for the integration of spatial statistical modeling and decision analysis for evaluating information gathering schemes. The value of information is computed before purchasing data, and can be useful for checking if data acquisition or processing is worth its price, or for comparing various experiments.
The course will build a framework of perfect versus imperfect information, and total versus partial information where only a subset of the data is acquired or processed. This is studied and compared with alternative information criteria such as entropy, variance and prediction error, which are also commonly used in applications.
The course uses slide presentations and runs hands-on projects on the computer (in Matlab and R). The examples demonstrate value of information analysis in various applications, including environmental sciences, petroleum and mining. In these situations the decision maker could make better decisions by purchasing information (at a price) via for instance surveying, borehole tests, seismic or electromagnetic data.