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degree in data science, Applied Mathematics, Computer Science, Physics, Meteorology, or a related field. Strong interest in wind energy development and the energy transition. Proficient in Python
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topics and projects with the following activities: Implementation of ML models for optimizing hedging strategies in Python Evaluation and comparison of different models (benchmarking) Supporting data
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the first 5 years, also including a summary of previous work, collaborations, and wider outlook (maximum 5 pages) Contact information for at least three academic referees The position is permanent. Contact
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Technology: Professorship Measurement and Sensor Technology Further information Technische Universität Dresden Faculty of Electrical and Computer Engineering Faculty of Computer Science Faculty of Mechanical Science and
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reference data, and explore how different stack and channel designs affect shunt current distribution. Ultimately, the results should contribute to optimizing cell and stack configurations for minimal energy
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analysis of companies in the semiconductor industry. You will apply Generative AI technologies to automatically analyze data and identify market trends. You will collect and evaluate relevant scientific and
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by other funding providers: Further information Selection An independent selection committee consisting of professors from German universities or research facilities reviews applications. Central
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qualified. Information on job advertisements and the collection of personal data is available at www.uni-heidelberg.de/en/job-market .
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development and immune responses. The role involves conducting wet lab experiments to generate data essential for modeling the kinetics of MZ B cells. This is a collaborative project with renowned international
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, Computer Science, Data Science or a related discipline Solid knowledge of Probability Theory, Linear Algebra, and Analysis Experience with PyTorch; familiarity with probabilistic modeling and Gaussian Mixture Models