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Field
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machine learning (ML), artificial intelligence (AI) or related fields • Software skills in ML languages such as Python • Ability and enthusiasm to learn new technologies quickly • Ability to work highly
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data Knowledge of statistical methods in the context of biological systems Experience with programming (Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will
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practical experience in at least one programming language (preferably Python) Ideally, some practical experience in material characterization methods Structured and analytical thinking as well as a systematic
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of X-ray methods Knowledge of X-ray optics Knowledge of synchrotron science Knowledge of catalysis and energy storage Experience with programming languages (ideally Python), SPS controls system Fluent in
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with shallow water equations). Python coding for workflow control, data pre- and post-processing as well as model calibration and validation. High-performance computing (HPC) for running test cases and
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Retrieval-Augmented Generation (RAG) for data retrieval and knowledge inference implementation of your machine learning pipeline in Python (using e.g. PyTorch) validation of your results in collaboration with
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or similar, obtained by the start date Experience in modeling using Python, MATLAB or similar Basic programming skills Proficiency in scientific English (written and spoken) Willingness to spend several months
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language models (LLMs) Proficiency in Python programming and confident use of Unix/Linux environments; ideally experience with version control systems (e.g., Git) Interest in or experience with semantic web
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microscopy data is an asset but not required Interest in foundational machine learning research with applied impact in scientific imaging Demonstrated proficiency in Python and experience with ML/DL frameworks
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; Experience with HPC, programming (e.g., Python, C/C++), and/or scientific computing is a plus Strong interest in quantum computing and molecular simulations Willingness to work in an interdisciplinary