Jobs
We invite applications for open student research assistant, PhD, and PostDoc positions.
- Student Research Assistants: We are looking for student research assistants with strong skills in Python and/or strong knowledge in deep learning frameworks such as tensorflow.
- PhD positions: The applicant should have a degree (diploma or master) in computer science, mathematics, or physics. The position requires a very strong background in mathematics, particularly linear algebra, statistics, probability, and optimization, and strong analytical skills. Programming experience in Python, C++, or MATLAB is required. Knowledge in a deep learning framework like tensorflow is a plus. A background in machine learning is appreciated but not absolutely necessary.
- PostDoc positions: You have a strong publication record in the top journals/conferences of eitehr machine learning (NIPS, ICML, COLT, CVPR), statistics, or optimization. Moreover, you have a very good background in mathematics.
Applications (including CV and publication list) should be sent by email as a single PDF file to: matthias.hein@uni-tuebingen.de
Special announcement for joint project with Christian Baumgartner:
The Machine Learning in Medical Image Analysis group (led by Christian Baumgartner) and the Machine Learning group (led by Matthias Hein) at the University of Tübingen, Cluster of Excellence ‘Machine Learning: New Perspectives for Science’ invites applications for an open
Doctoral-Position (m/f/d, E13 TV-L; 65%) in Robust Machine Learning for Medical Image Analysis
to be filled as soon as possible.
Project description:
The aim of this project is to develop rigorous, mathematically founded techniques to assess the robustness of automated medical image analysis systems and to investigate methods for providing provable guarantees of an algorithms performance under variations in the image acquisition process.
Medical imaging data very often are subject to systematic changes in appearance originating from different acquisition parameters or different imaging hardware. Unfortunately, modern deep learning systems have been shown to be extremely sensitive to such variations, to the point where an algorithm trained with data from one hospital, may not work on data acquired at a different hospital. Formally assessing the robustness of machine learning methods, building more robust techniques, and providing guarantees for the performance of such techniques is of utmost importance for these methods to be eventually deployed in clinical practice.
Thus, the successful candidate will contribute directly to one of the big unsolved problems hindering wide-spread adoption of AI technology for medical image analysis.
Who we are looking for:
You are curious, enjoy analytical thinking and have a passion for science. You have a strong motivation to do machine learning research as well as a keen interest to solve real-world clinical problems. You hold a M.Sc. degree (or similar) in machine learning, mathematics, statistics, physics, computer science, or similar fields. Ideally, you have prior experience with deep neural networks, and strong programming skills in Python.
What we offer:
This is a project jointly supervised by Prof. Matthias Hein and Dr. Christian Baumgartner and thus is truly at the intersection between state-of-the-art machine learning and medical image analysis. The successful candidate will work at the Cluster of Excellence "Machine Learning - New Perspectives for Science" and will benefit from this vibrant research environment as well as from the activities and events organized by the cluster and associated institutions.
About Tübingen
Tübingen is a scenic university town on the Neckar river in South-Western Germany. The quality of life is exceptionally high and the atmosphere is diverse, inclusive, and most locals speak English. Tübingen offers excellent research opportunities due to the University, four Max Planck institutes, the University Hospital, and Europe’s largest AI research consortium. You can find out more about Tübingen here: https://www.tuebingen.de/en/
How to apply:
Please send a cover letter, your CV, the names and email addresses of 2-3 referees, and unofficial copies of your University transcripts to Christian Baumgartner (christian.baumgartner@uni-tuebingen.de). If you have any questions about the position, please do not hesitate to contact Christian directly. The university seeks to raise the number of women in research and teaching and therefore urges qualified women scientists to apply for these positions. Equally qualified applicants with disabilities will be given preference. The employment will be carried out by the central administration of the University of Tübingen. Please submit your application by May 2nd, 2021.
The Machine Learning in Medical Image Analysis group (led by Christian Baumgartner) and the Machine Learning group (led by Matthias Hein) at the University of Tübingen, Cluster of Excellence ‘Machine Learning: New Perspectives for Science’ invites applications for an open
Doctoral-Position (m/f/d, E13 TV-L; 65%) in Robust Machine Learning for Medical Image Analysis
to be filled as soon as possible.
Project description:
The aim of this project is to develop rigorous, mathematically founded techniques to assess the robustness of automated medical image analysis systems and to investigate methods for providing provable guarantees of an algorithms performance under variations in the image acquisition process.
Medical imaging data very often are subject to systematic changes in appearance originating from different acquisition parameters or different imaging hardware. Unfortunately, modern deep learning systems have been shown to be extremely sensitive to such variations, to the point where an algorithm trained with data from one hospital, may not work on data acquired at a different hospital. Formally assessing the robustness of machine learning methods, building more robust techniques, and providing guarantees for the performance of such techniques is of utmost importance for these methods to be eventually deployed in clinical practice.
Thus, the successful candidate will contribute directly to one of the big unsolved problems hindering wide-spread adoption of AI technology for medical image analysis.
Who we are looking for:
You are curious, enjoy analytical thinking and have a passion for science. You have a strong motivation to do machine learning research as well as a keen interest to solve real-world clinical problems. You hold a M.Sc. degree (or similar) in machine learning, mathematics, statistics, physics, computer science, or similar fields. Ideally, you have prior experience with deep neural networks, and strong programming skills in Python.
What we offer:
This is a project jointly supervised by Prof. Matthias Hein and Dr. Christian Baumgartner and thus is truly at the intersection between state-of-the-art machine learning and medical image analysis. The successful candidate will work at the Cluster of Excellence "Machine Learning - New Perspectives for Science" and will benefit from this vibrant research environment as well as from the activities and events organized by the cluster and associated institutions.
About Tübingen
Tübingen is a scenic university town on the Neckar river in South-Western Germany. The quality of life is exceptionally high and the atmosphere is diverse, inclusive, and most locals speak English. Tübingen offers excellent research opportunities due to the University, four Max Planck institutes, the University Hospital, and Europe’s largest AI research consortium. You can find out more about Tübingen here: https://www.tuebingen.de/en/
How to apply:
Please send a cover letter, your CV, the names and email addresses of 2-3 referees, and unofficial copies of your University transcripts to Christian Baumgartner (christian.baumgartner@uni-tuebingen.de). If you have any questions about the position, please do not hesitate to contact Christian directly. The university seeks to raise the number of women in research and teaching and therefore urges qualified women scientists to apply for these positions. Equally qualified applicants with disabilities will be given preference. The employment will be carried out by the central administration of the University of Tübingen. Please submit your application by May 2nd, 2021.