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. Expertise in at least one of the following subjects is desired: Inverse Problems, Numerical Analysis of PDEs, Bayesian Statistics, MCMC, and experience in high level programming (Python). The postdoc will be
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of the GRISLI model Code programming in Python and Fortran Write scientific articles - good level of English required Analysis of a large set of GRISLI simulations and sensitivity study Presentation of results
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. Strong background in neuroimaging techniques (e.g., MRI, PET) and computational modeling. Proficiency in programming languages such as Python, MATLAB, or R. Experience with image processing tools and
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Python, MATLAB, or C++. Experience with machine learning techniques, CAD, computational modeling, 3D printing, motion capture, and/or material testing. Proficiency in programming languages commonly used in
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analysis, and proficiency in statistical and computer modelling software (e.g. R, Python, Matlab, and C++) would be advantageous. The appointee will work with a research team to study the methodologies
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judged from published papers or publicly available (e.g., arXived) preprints, will be given priority. Strong programming skills in R/Python/MATLAB with some knowledge of C/C++ will be preferred. Prior
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Bayesian statistics, stochastic modeling, and optimization under uncertainty • Proficiency in Python programming • Strong interest in applied and transferable research • Knowledge of industrial simulation
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machine learning and statistics; experience with Gaussian process regression and/or probabilistic regression. Experience with normative modelling is an advantage. Proficiency in Python (and ideally C/C
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background in machine learning, predictive modeling, or applied AI Proficiency in Python and/or R; experience with libraries like scikit-learn, XGBoost, TensorFlow. -Experience working with real-world datasets
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and active‑heating rigs; Strong programming skills (Python/Matlab and/or FEM packages); Publication record appropriate to career stage; Excellent teamwork, project‑management and communication skills