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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 3 months ago
programming, preferably Python and R, is required. Experience with mass spectrometry data, in particular metabolomics, and geometric machine learning is a plus. In addition to above-average interest in
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), and GraphRAG approaches Hands on experience with Python Excellent English language skills, and Motivation to join an interdisciplinary and international research environment. This vacancy is open to all
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Hands on experience with Python Excellent English language skills, and Motivation to join an interdisciplinary and international research environment. This vacancy is open to all persons, irrespective
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the analysis and simulation of analog, digital, or mixed-signal circuits, including SPICE and related tools (LTspice, Cadence, MATLAB, Python) Excellent communication skills and ability to work in a team are
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meteorology, physics, geosciences or related field excellent programming skills (preferably in FORTRAN or Python) interest in interdisciplinary climate and coastal research We offer you an exciting and varied
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of numerical methods for multi-physics application, specifically using Chimera/overset meshes and with applications to shocks Experience in programming in Python and C++, and in using scripting languages
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, materials science and/or process engineering systems is an advantage Knowledge of relevant programming languages for data processing/evaluation, especially Python/ Matplotlib, is an advantage Independent
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simulation models Programming skills (e.g. Matlab or Python) Willingness to supervise young scientists Enthusiasm and enjoyment of working independently on developing fresh ideas and solutions Good
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skills. Experience with programming, preferably Python and R, is required. Experience with deep learning frameworks, such as JAX or PyTorch, is a plus. In addition to above-average interest in the topic
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skills. Experience with programming, preferably Python and R, is required. Experience with mass spectrometry data, in particular metabolomics, and geometric machine learning is a plus. In addition to above