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Mathematics (analysis, numerics, modeling) or in a comparable program with a strong mathematical focus and knowledge in, for example, functional analysis as well as the theory and numerics of PDEs. Strong
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related field PostDoc track: PhD in a relevant discipline with a record in medical image analysis / ML Strong programming skills (e.g., Python, PyTorch/TensorFlow; image processing frameworks) Background in
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or engineering. Fluency in spoken and written English is required. Proficient in at least one programming language, e.g. MATLAB, C/C++, Python. Highly motivated and keen on working in an international and
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or Postdoc Position in Numerical Mathematics m/f/d, 100%, 2 years+ As part of the second phase of the DFG funded Priority Programme SPP2311, the Chair for Numerical Mathematics under the leadership
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to contribute to groundbreaking work in our research area. About the Program: We are implementing a program that creates an additional two year postdoctoral position exclusively for female researchers working in
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technologies to fundamental physics questions. The advertised positions will be part of the project “QS-Gauge: quantum simulation of lattice gauge theories”, funded by the Emmy Noether programme of the DFG
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Engineering, Computer Engineering, Computer Science, or a closely related field Strong background in robotics fundamentals: kinematics, dynamics, control, planning Proficiency in programming (C++, Python), and
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programming language, e.g. MATLAB, C/C++, Python. Highly motivated and keen on working in an international and interdisciplinary team. Applicants with strong background in the following fields are preferred
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% international students in the MA program. The visual computing lab embraces its diverse culture, and is proud to host PhD students from over 10 different countries. Our lab language is English. The position is
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research studies for automated image analysis. In particular, you will: Plan, develop, and implement AI/ML algorithms for pathology image analysis. Integrate multi-modal data (e.g., genomics, clinical data