15 programming-"the"-"DAAD"-"IMPRS-ML"-"Lancaster-University"-"Prof.-Dr" Postdoctoral positions at Technical University of Munich in Germany
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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 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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% 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
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with large-scale data analytics frameworks (Hadoop, Spark, Flink, etc.) is desired - Interest in the development of software systems, very good knowledge and skills in programming with standard
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machine and deep learning Programming experience with Python and Pytorch Strong analytical and problem-solving skills Excellent communication & interdisciplinary skills Fluency in English (written and
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Institute (https://www.mdsi.tum.de/). The Position Plan, develop and test novel computational models for the analysis of digital pathology image data. Collaborate with pathologists and other domain experts