PhD and postdoc positions at the interface of machine learning, physics and imaging. 100% / 1st January 2020

Updated: 9 days ago
Deadline: 01 Jan 2020

PhD and postdoc positions

at the interface of machine learning, physics and imaging. 100% / 1st January 2020


We are looking for creative, open-minded PhD students and postdocs to work on problems between machine learning, physics and imaging at the Department Mathematics and Computer Science. The positions are funded by an ERC Starting Grant Signals, Waves, and Learning-A Data-Driven Paradigm for Wave-Based Inverse Problems (SWING). Our mission will be to develop machine learning and signal processing tools relevant for scientific questions in imaging and inverse problems. We will seek both theoretical insight and impact in key applications.
Several PhD and postdoc positions are available. The postdoc openings include supervision of student projects and help with running the group.


Your position
The ERC project addresses the theory of learning for inverse problems together with the cutting-edge applications. The objectives of the project are 1) to combine ideas from two centuries of mathematical physics with modern machine learning, and 2) to pursue new results in signal processing, including unlabeled and non-linear sampling, randomized sensing (as in random-view tomography), and corresponding invariant computational structures. We will look at applications across the imaging spectrum, including molecular, medical, and seismic. Individual projects will address different aspects of this story. As a team we will strive for a fundamental understanding of the interplay between machine learning, signal processing and physics.


Your profile
We are looking for candidates with a strong academic record in computer science, electrical engineering, physics, numerical analysis, or related disciplines. They should be able to write good code in Python and C/C++. Exposure to one of the popular deep learning frameworks, prior research experience, and interest in signal processing and inverse problems are all desirable assets.


We offer you
University of Basel is the oldest university in Switzerland with an exceptionally strong presence in math, imaging, and life sciences including the relevant high-end experimental facilities. The math department is associated with giants like the Bernoulli brothers and Leonhard Euler. Working in our group includes a stimulating research environment, plentiful opportunities for collaboration in Europe (EPFL, ETHZ in Switzerland, ENS Paris, ENS Lyon, TU Munich) and in the United States (UIUC, Rice, MIT), traveling to workshops, conferences, summer and winter schools, and a friendly, fun, and productive group atmosphere.


Application / Contact
Applications should be sent directly to Prof. Dr. Ivan Dokmanić (ivan.dokmanic@unibas.ch). Please include a short statement of motivation describing your interest in the project areas, a curriculum vitae, grade transcripts, and a contact information of a possible reference.

www.unibas.ch




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