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, or Mining Engineering, with demonstrated link/application to the mineral raw materials sector. The candidates should demonstrate some of the following skills: - coding in Python - Life Cycle Thinking (LCA
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 2 months ago
methods. Strong programming skills, preferably in Python, and experience working with scientific data. Strong interest in using computational methods to discover biological or medical insights from imaging
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engineering disciplines, including structural mechanics, hydrodynamics and machine learning Strong programming skills in Python and/or MATLAB Experience with scientific computing, CFD/FEM software, potential
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interpretable artificial intelligence (preferred). You have experience with coding in Python using deep learning toolkits (e.g., PyTorch). You have good analytical and problem-solving abilities. You are
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experience with coding in Python using deep learning toolkits (e.g., PyTorch). You have good analytical and problem-solving abilities. You are proficient in English (written and spoken). What we offer We offer
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learning. You have experience with coding in Python using deep learning toolkits (e.g., PyTorch). You have good analytical and problem-solving abilities. You are proficient in English (written and spoken
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, or laser instrumentation is appreciated - programming skills for data acquisition and analysis (Python) - interest in quantum technologies and solid-state quantum emitters. - strong motivation
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and/or hydraulic laboratories Experience with experimental research and data analysis Analytical and programming skills for scientific computing and data analysis (e.g. Python, MATLAB, R, or similar
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(Python, C++, etc.). Experience in mathematical programming and/or optimization software such Pyomo, GAMS or similar. Proficiency in process simulation software (Aspen Plus, Aspen HYSYS, etc.). Experience
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other omics data. Programming experience in R and/or Python, as well as familiarity with Unix/Linux-based computational environments and reproducible research workflows. Interest in integrating genetic