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Postdoc in assessing carbon sequestration potential of different wetlands as nature-based solutio...
qualifications: A solid background in programming using Python, R, or other languages. Teaching and supervision experience at the BSc and MSc level. Interest and experience in developing competitive national and
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qualifications: A solid background in programming using Python, R, or other languages. Teaching and supervision experience at the BSc and MSc level. Interest and experience in developing competitive national and
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co-simulation platforms (e.g., TRNSYS-Python) Implement and test AI-enabled smart energy management strategies in real-world settings Conducting in situ measurements, including planning, setting up
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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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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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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