Scientific computing with its core components mathematical modeling, simulation and optimization has developed into a key technology for understanding and mastering challenges in science and engineering. Stemming from application problems as diverse as the design of fuel cells, the understanding of the dynamics of cancer or the risk analysis for historical monuments, the demand for young scientists who are well-trained in these methods and application fields is rising fast.

The HGS MathComp intends to meet this demand. Our aim is to provide a structured interdisciplinary reserach training program to promote the developement of new and even more powerful methods of scientific computing and to carry this methodology into new scientific territory. Our students will receive training based on the guiding principles of

• Scientific excellence
• Interdisciplinarity
• Internationality

The ambition is to provide the doctoral students with excellent training that equips them with the manifold qualifications required from future leading scientists.

The Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences is funded by the German Research Foundation (DFG) in the second round of the Excellence Initiative.

For further information please visit the HGS MathComp website.

Probabilistic graphical models provide a consistent framework for the statistical modeling and the computational analysis of scientific empirical data. The past decade has witnessed a significant increase in respective research in the field of image analysis and related application areas, driven by the synergy between statistics, pattern recognition, computer vision and machine learning. The objective is to devise models that enable to infer a coherent global interpretation of noisy and ambiguous local image measurements, taking into account spatiotemporal context in images and videos, and domain-specific contextual knowledge.

Applications of probabilistic graphical models to such large-scale problems raise numerous research problems of modeling and algorithm design for inference and learning, requiring interdisciplinary expertise in applied mathematics, computer science and physics, besides a profound knowledge of the respective application areas.

The Research Graduate School, located at the Heidelberg Collaboratory for Image Processing (HCI), offers an additional branch of doctoral projects in the specific field of image processing, supported by HGS MathComp. The basic intention of the Research Training Group is to gather experts from these fields and to establish a coherent research and study program on probabilistic graphical models, with a focus on spatial and spatiotemporal models and their applications in image analysis. The project treats methodological basic research on an equal footing with challenging scientific applications of image analysis in environmental science, life sciences and industry.

The Research Training Group provides a scientifically unique environment for study, collaboration and innovative research on probabilistic graphical models across disciplines, producing highly-qualified candidates for research careers in academia and industry.

For further information please visit the RTG 1653 website.