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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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electrical or mechanical engineering Strong mathematical skills Experience in modelling energy systems Very good knowledge and experience in programming (e.g. Python, Matlab, C, C++) Fluent in written and
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, uncertainty modeling, or decision-making under constraints. Experience with Python and modern ML frameworks such as PyTorch or TensorFlow. Curiosity for interdisciplinary research; prior experience with
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Proficiency in at least one programming language (Python, C++, …) Keen interest in neuroscience is essential Experience with modelling, analysis of complex dynamical systems, simulation, analysis of large-scale
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well as experience in omics data analysis, and possesses solid English-language skills. Experience with programming, preferably Python and R, is required. Experience with deep learning frameworks, such as JAX
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, please visit: https://qbm.genzentrum.lmu.de/application/ Tuition fees per semester in EUR None Combined Master's degree / PhD programme No Joint degree / double degree programme No Description/content
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problems Proficiency in data analysis and programming using at least one statistical program such as R, Python, or similar programming languages Experience with GAMS, GTAP, and Exiobase is an asset. Skills
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continuum within the 2nd phase of CLICCS (https://www.cliccs.uni-hamburg.de/about-cliccs/cliccs-ll.html). In CLICCS-M4, we are further developing the unique ICON-Coast model within the ICON Earth System
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engineering Very strong mathematical and algorithmic background Programming experience (Python, C++, etc.) Familiarity with parallel programming frameworks (e.g. MPI, CUDA) Fluent in written and spoken English
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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