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areas: Knowledge of computer science and operations research Familiarity with renewable energy systems and their challenges Proficiency in programming languages such as Python or Julia Strong problem
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experience with statistical tools (e.g. in R, MatLab, or Python) are expected. The team at DTU Aqua is highly international and knowing the Danish language is not needed. You must be available for boat-based
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Experience with Fortran, Python and Linux Shell Experience working with large datasets Preferred but not essential: Knowledge of wind farm parameterizations in mesoscale models Experience in the field of wave
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experience in the following fields. Cyber-physical modelling and simulation Digital Twins Autonomous Agents and Multi-Agent Systems Machine Learning and MLOps Probability & Statistics incl. Python/R Place of
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and robotics). Proficiency in Python, MATLAB, and/or C++ programming dialects (knowledge in other languages will be valued). Marine fieldwork experience with deployed platforms is an advantage. You must
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equivalent to a two-year master's degree. Additional qualifications include: Good programming skills in Python, Julia, R or similar, and familiarity with C, C# or C++. Curiosity and interest in future urban
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spectrometry, and untargeted data science workflows. Proficiency in chemometric methods and/or python programming will be an advantage. Candidates are expected to be enthusiastic and adaptable to working in
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. Documented experience with any project in the blue bioeconomy domain, with Life Cycle Assessment, and with programming for data science(R, python) will be considered a strong plus. Proficiency in English is a
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protein design programs and analysis tools is crucial. Knowledge of programs like Rosetta, RF-diffusion, FoldX and python is necessary. Apart from very good modeling skills, knowledge in protein production
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., linear algebra, statistics, optimization, and calculus) is expected, along with programming experience using deep learning frameworks in Python (e.g., PyTorch). While prior knowledge of machine learning