The bachelor's thesis is the culmination of the program. The students develop, enhance, and demonstrate their methodological abilities to independently tackle and solve a given research problem. The thesis furnishes the students with their first major research experience, and is a further development of the work done in the basis courses, and usually, the focused study.

This lecture focuses on the foundations of bio-inspired optimization. The exercises will be oriented towards the implementation of these concepts to design applications.

Study of mathematical methods and algorithms in bioinformatics: Topics: Probability and statistics (prerequisites, statistical estimation, Markov chains, evolutionary models, sequence alignment), Hidden Markov models (Viterbi algorithm), Bayesian networks (principles, network inference), sequence alignment and phylogenetic trees (evolutionary relations, multiple sequence alignment, tree building).

The course emphasizes fundamental physical principles and focuses on the way these principles dictate the structure and function of cells. The course topics address biological concepts rooted in quantitative biological experimental data and it aims to provide the tools for a quantitative and predective understanding of cellular life.

The school (1.9. - 12.9.2008) will discuss the recent progress and challenges in biological and medical imaging. Cutting edge techniques using a wide range of imaging mechanisms will be put in the context of selected biomedical problems. In particular, multimodal and multiscale imaging methods as well as supporting technologies such as computer aided image analysis and modeling will be discussed.

Class participants study and make a 40 minute presentation (in English) on fundamental papers of Computational Science. A preliminary discussion of the talk (structure, content, methodology) with the responsible professor is required. The talk has to be given in a way that the other seminar participants can understand it and learn from it. Participation throughout the semester is mandatory.

Class participants study and make a 40 minute presentation (in English) on fundamental papers of Computational Science. A preliminary discussion of the talk (structure, content, methodology) with the responsible professor is required. The talk has to be given in a way that the other seminar participants can understand it and learn from it. Participation throughout the semester is mandatory.

Class participants study and make a 40 minute presentation (in English) on fundamental papers of Computational Science. A preliminary discussion of the talk (structure, content, methodology) with the responsible professor is required. The talk has to be given in a way that the other seminar participants can understand it and learn from it. Participation throughout the semester is mandatory.

Students will be presented an overview of computer organization anddesign. Using the assembly language MIPS the organization levels fromlogic gates to the data path are described. Additional topics fromtheoretical and practical computer science are Turing machines,information theory, computer networks and data bases.

This course gives an introduction into algorithms and numerical methods for parallel computing on shared and distributed memory architectures. The algorithms and methods are supported with problems that appear frequently in science and engineering.

This course focuses on programming methods and tools for modern parallel systems, such as large-scale supercomputers with multi and many-core processors. Emphasis will be placed on techniques and models to maximize the performance of such systems. This is a hands-on course that relies on practical applications in science and engineering to demonstrate the importance of HPC.

This course focuses on programming methods and tools for parallel computing on multi and many-core architectures. Emphasis will be placed on practical and computational aspects of Uncertainty Quantification and Propagation including the implementation of relevant algorithms on HPC architectures.

Non-linear equations, Fundamentals of interpolation (points and functions), Nonlinear Least Squares, Optimization, Introduction to Symbolic computation.

Numerical Quadrature: Methods of numerical integration, Euler-Mac Laurin summation. Ordinary differential equations: discretization, error analysis, multistep methods, Runge-Kutta methods, adaptive methods. Numerical Differentiation: numerical derivatives by finite differencing, algorithmic differentiation. Introduction to Partial Differential Equations.

Fundamentals of multiresolution and multiscale modeling and computation.Coupling of physical descriptions across different scales andmultiresolution computational methods.Multiscale concepts are introduced using examples from engineering and scientific problems.

Fundamentals of multiscale modeling and computation with emphasis onthe coupling of physical descriptions across different scales andon multiresolution computational methods. Multiscale concepts are introducedusing examples from engineering and scientific problems.

The course provides a unifying framework for particle simulations of discrete and continuum systems. Recent advances in molecular, mesoscopic and macroscale simulations using particles will be discussed and common computing paradigms and challenges across disciplines identified.

Current advanced research activities in the areas of thermo- and fluid dynamics are presented and discussed, mostly by external speakers.

The course presents fundamental concepts and advanced methodologies for handling and interpreting data in relation with models. It elaborates on methods and tools for identifying, quantifying and propagating uncertainty through models of systems with applications in various fields of Engineering and Applied science.