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background in thermodynamics and phase behavior of complex mixtures Excellent programming skills (e.g., Python, C++, Fortran, or similar) Experience with COSMO-based methods, including parameterization, model
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software skills Proven experience with CFD automation, e.g. using PyFluent or similar Python APIs for meshing, solver control, post-processing and data management. Solid experience programming in Python
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programming experience with Python and/or Matlab Experience with advanced MEG/EEG signal processing and/or machine learning is desirable Strong interest in translational research and developmental neuroscience
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(e.g., R, Python). Proven ability to publish at a high international level. It is a prerequisite that you are good at communicating in English. Strong collaborative skills and good collaboration skills
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qualifications: Strong experience in programming using Python, R, or other languages Research experience in remote sensing of cover crop, crop type classification, and crop biomass Insight into global
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qualifications: Strong experience in programming using Python, R, or other languages Research experience in remote sensing of cover crop, crop type classification, and crop biomass Insight into global
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and Python are required. The successful candidate will be based in Odense, under the primary supervision of Prof. Stefan Jänicke. The appointment will be made for 2 (two) years at a competitive salary
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Quantification Python and ML frameworks (TensorFlow, PyTorch, JAX) Reproducible and open-science practices Experience with geospatial, environmental, or climate data is advantageous but not required. What We Offer
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languages such as Python or R. Experience with machine learning, systems biology, or network modeling approaches. Previous expertise in human cardiometabolic or complex diseases, with domain expertise in but
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Stata; Knowledge of R, Matlab, Python, and/or Fortran; Experience working with micro data, ideally administrative or matched employer–employee data; Documented research track record at international level