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and actual work settings. Tasks include: Developing research paradigms Programming experiments and analyzing data Presenting results at conferences and writing research articles Supervising research
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of linear algebra and numerical optimization Understanding of statistical modeling and inverse problems is desirable Experience with programming languages like Python, MATLAB, or C++ Joy in dealing with
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for livestock systems in East Africa, and in the subtropics in Latin-America. The research programme will examine productivity of grasslands, nutrient stocks and cycling and their relationship to biodiversity. We
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of empirical research (quantitative or experimental) methods, • knowledge of statistics, programming languages (e.g., Python), natural language processing, machine learning is advantageous but not
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and interdisciplinary research questions. Good communication skills in English and/or German are expected. What we offer: WIAS Berlin is a premier research institution known for its strength in
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student assistants and contribute to shaping the CRC’s research direction Your Profile PhD in computer science, neuroscience, machine learning, or related field Strong programming skills in Python and
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Max Planck Institute for Plasma Physics (Garching), Garching | Garching an der Alz, Bayern | Germany | 2 months ago
the experimental data with support of simulations Present the results in meetings, conferences and reports Active contribution to the experimental program to obtain beam properties Strong
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development. Experience with implementing statistical learning or machine learning (e.g. Bayesian inference, deep-learning). Programming skills in Python and experience with frameworks like PyTorch, Keras, Pyro
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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
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part of a degree program. In particular, knowledge about finite-element analysis is an absolute must . Familiarity with iterative solvers , preconditioners , multigrid methods , and mixed-precision