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into consideration. The applicant should have good knowledge of automatic control, optimal control, statistics, and optimization. Furthermore, good programming skills are required, including Python and C++, and
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: Strong knowledge of building physics and HVAC system Knowledge of automatic control Programming skills (e.g., Python, R, MATLAB) Fluent English skills, both written and spoken. Contract terms and what we
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networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid academic background with thorough computational and
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skills in Python or C++ Experience in machine learning frameworks (for example PyTorch) Skills in computer vision or machine learning relevant for the project Consideration will also be given to good
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of modelling and programming skills in e.g. Python C/C++, MATLAB and/or LabView. Basic knowledge in electronics. 3D CAD (such as Autodesk Fusion 360, OpenSCAD or similar). Experience in project management
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Sciences, or a related field. Strong programming skills in Python Good understanding of chemistry and biology concepts Excellent communication skills in English (oral and written) Desirable skills/experience
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: Master’s degree in biomedicine or biostatistics. Doctor of medicine degree with clinical practice experience. Certified training in R and Python software. Documented experience using machine learning and
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, dynamic programming , statistical signal processing, reinforcement learning, and have good programming skills in Python and MATLAB. - Ability to work independently and ability to formulate and tackle
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, evolutionary, and biogeographical processes Proficient in modeling dynamic systems (e.g. ODE-models or IBMs) Experience in programming, e.g. in Matlab, Python, C++, R etc. A strong interest in and experience
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Python or R are required. Basic understanding of statistical analysis in large dataset is expected. Fluency in written and spoken English. Merits: Experience in applying machine learning and/or network