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. Responsibilities and qualifications Qualifications: PhD degree in Engineering, Physics, Computer Science, or Applied Mathematics. Proficiency in scientific programming with Python. Excellent oral and written
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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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. Programming or scripting skills (e.g., Python, R, or MATLAB) for exposure modeling or database automation. Other Competencies Ability to work in interdisciplinary, international teams. Strong communication and
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or related field. Expertise in graph machine learning and demonstrated experience in multi-omics data integration. Strong programming skills in Python and its scientific and graph ML libraries (numpy, pandas
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degree and sufficient work experience would also be considered. Proficiency in Python for data analysis. Experience interfacing databases and R scripting will be an advantage but not required. Familiarity
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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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technology Experienced with engineering coding in Python and/or MATLAB Technical knowledge of camera hardware, such as RGB and thermal cameras Hands-on knowledge of drones and their operation, A1/A3 license
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English, both written and spoken. Additional programming experience is advantageous but not required: If you are not afraid to use python or R for analyzing and processing data, that’s a plus. As a formal
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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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operators, transformers/LLM) and NN training. Strong Python programming skills (as a plus: C++ or Julia) and knowledge of scientific computing libraries (numpy, scipy, JAX…) and machine learning libraries