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digital co-simulation platforms (e.g., Modelica-Python/Simulink) Applying machine learning and data-driven approaches to enhance the operation of district heating substations Participating in course
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-based and farm models with a focus on biogeochemical and hydrogeological fluxes. Knowledge of greenhouse gas inventories (methane, ammonia, nitrous oxide) Proficient skills with scripting (R, Python) and
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: PhD in computer science, machine learning, operations research, transportation engineering or a related field. Programming skills in C/C++ and Python, along with experience working with simulation
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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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-based and farm models with a focus on biogeochemical and hydrogeological fluxes. Knowledge of greenhouse gas inventories (methane, ammonia, nitrous oxide) Proficient skills with scripting (R, Python) and
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NextFlow is a plus. Programming experience is required (Python, R). Previous experience in Genomics data analysis Strong analytical, organizational, and record-keeping skills Interest in working in a
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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