Probabilistic Machine Learning and Medical Image Processing Saarland University, Saarbruecken, Germany
Fully-funded PhD/postdoc positions are available in the recently established Probabilistic Machine Learning group headed by Matthias Seeger (PhD). PhD training is conditional on acceptance to the International Max Planck Research School for Computer Science (based on evaluation of research proposal and oral presentation, after first six months).
Recent breakthroughs in large-scale approximate Bayesian inference for sparse continuous variable models allow nonlinear Bayesian experimental design (active learning) and compressed sensing to be applied to sampling optimization of magnetic resonance imaging. More detail about these projects can be found at the following link
Saarland University is among the leading computer science faculties in Europe, with world-class groups in computer graphics, theory of algorithms and programming languages, theoretical CS, and bioinformatics, among others. It features a unique accumulation of top-ranked CS research institutes (Max Planck Institute for Informatik, Max Planck Institute for Software Systems, DFKI). Within the recently established interdisciplinary MMCI Cluster of Excellence, 20 independent research groups are working in areas with strong overlaps to core machine learning application areas. Saarbruecken is dedicated to excellent postgraduate education, structured according to international standards in the International Max Planck Research School for Computer Science (courses taught in english).
The Probabilistic Machine Learning group focusses on theory and applications of approximate Bayesian inference, and its scalable reduction to standard methods of scientific computing (numerical mathematics, efficient algorithms, signal processing, parallel computing). We closely collaborate with the Center for High-field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tuebingen (with a range of MR scanners dedicated to basic research), and have close ties to the Empirical Inference group (headed by Bernhard Schoelkopf) at the same institute, beyond connections to top machine learning groups in the UK and US.
We are looking for highly motivated, research-oriented individuals with an excellent grasp of the mathematics underlying approximate Bayesian inference, or/and numerical optimization and mathematics, or/and image and signal processing. A strong theoretical background in a field relevant to analysis of statistical methods, or/and keen interest and capabilities in large-scale scientific programming are required.
Please be sure to include the following in your application:
– Curriculum vitae
– Statement of research interests (1 page)
– Letters of reference (1-3) from referees able to comment on your work and academic standing (PhD/MSc thesis advisor, supervisor for internships)
– Sample of your strongest work (first-author paper in peer-reviewed journal/conference, MSc or PhD thesis, term project paper (with official record attesting your authorship)) in the rough area of interest
– Transcript of studies (for PhD applicants)
Applications should be sent by e-mail to Matthias Seeger, email@example.com. If you happen to attend the forthcoming Neural Information Processing Systems conference (Vancouver, December 8-13, 2008; http://nips.cc/Conferences/2008/), please make yourself known to Matthias there.
MMCI Cluster of Excellence
International Max Planck Research School for Computer Science
Max Planck Institute for Informatik