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Field
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for candidates with interests in multiscale simulations of complex physical phenomena, from the atomistic/electronic scale to mesocopics and beyond. Of particular interest is the development and application
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. This postdoctoral position at KTH focuses on developing advanced modelling frameworks and techno-economic analyses within two national research projects addressing virtual energy sharing and large-scale
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at various points in a subcritical model reactor operating at near-zero power and driven by an external high-intensity, high-reliability neutron source; 2) Extending the previous results to a power ADS loaded
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proficiency with longitudinal and regression methods (e.g., linear mixed models, generalized additive models), survival analysis, and meta‑analysis. ● Experience with version control and reproducible
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, remote sensing products, model simulations) to inform model development, calibration, and validation. Collaborate with a multidisciplinary team of hydrologists, Earth system scientists, and computational
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Germany | about 1 month ago
or almost completed) in Physics, Applied Mathematics, Complex System or a related field. Strong background in mathematical modeling, machine learning, or biophysical simulations. Demonstrated interest in
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atmospheric models WRF-Chem and/or CHIMERE, developing new descriptions of primary ice formation from sources such as aviation, wildfire emissions, and mineral dust. High-resolution simulations of selected case
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development, and implement mechanistic maternal–fetal PBPK models. The candidate will perform model development, qualification, and simulation studies. They will integrate dose–exposure–response modeling by
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Proficiency in using simulation software for process modeling (e.g., ASPEN, SuperPro Designer…). Familiarity with scaling up biotechnological processes and designing pilot-scale plants. Ability to analyze
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proficiency with longitudinal and regression methods (e.g., linear mixed models, generalized additive models), survival analysis, and meta‑analysis. ● Experience with version control and reproducible