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Retrieval-Augmented Generation (RAG) for data retrieval and knowledge inference implementation of your machine learning pipeline in Python (using e.g. PyTorch) validation of your results in collaboration with
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inference implementation of your machine learning pipeline in Python (using e.g. PyTorch) validation of your results in collaboration with colleagues from various application areas publication and
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, Origin, R, Python) Driving licence (class B) is desirable The position will be filled by 1 October 2025 at the earliest. We offer an exciting position with varied activities in an interdisciplinary working
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analysis and analytical data analysis workflows, together with other team members Implementing AI-based microscopy image analysis software as python packages Developing algorithms to deploy machine learning
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and statistical data analysis Excellent written and spoken English skills Experience with TMS and proficiency in relevant software (e.g., MATLAB, R, Python, or SPSS) is an advantage Key responsibilities
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occur, and how can the overloading of individual regions be counteracted? Your contribution to scientific analysis: Further develop existing energy system models in Python to accurately map and analyze
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academic area such as applied mathematics, computer science, physics, biomedical or electrical engineering or similar disciplines. Good programming expertise (Matlab, C++, Python or equivalent) and
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are expected. Knowledge in parallel programming is desirable. Prior knowledge in differential-algebraic equations, Gaussian processes or kernel based methods is a plus; programming experience in Python or C/C
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into account as disruptive events, and which strategies can be derived from this for a resilient system design. Your contribution to scientific analysis: Further develop existing energy system models in Python
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to submit the dissertation after 3 years and 9 months of research. Desired requirements specific to this project include: experience with a range of relevant computer programming languages such as Python, R