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be found on our website: www.fz-juelich.de/ibg/daten-ki/casa-sdl-dbe Your Profile: Master’s degree (or equivalent) in computer science, applied mathematics, physics, engineering, biology, plant and
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/ coding and data analysis software Independent, structured, and goal-oriented working style Please provide evidence of the qualifications required for the job by certificates and references. Please indicate
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30.06.2025, Wissenschaftliches Personal The Chair of Software Engineering for Business Information Systems (sebis) at the Technical University of Munich is looking for outstanding candidates
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-based metadata platform that you will help develop. In collaboration with stakeholders from energy research, you will develop methods to increase data and software interoperability, enabling the automated
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Femtosecond Time-Resolved Pump-Probe Spectroscopic Ellipsometry and Time-Resolved Photoluminescence Data evaluation (commercial software, programming own software) Deposition of ScAlN thin films using magnetron
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Your Job: Reinforcement Learning (RL) is a versatile and powerful tool for control, but often data-inefficient, requiring numerous updates and non-local information such as replay buffers and batch
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internships at KU Leuven (Prof. Rodolphe Sepulchre) and at TU Delft (Prof. Charlotte Frenkel) Participate in yearly retreats organized by the doctoral network participants Support the dissemination of software
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to increase data and software interoperability, enabling the automated reuse of energy systems analysis processes. In line with FAIR and Linked Open Data principles, you will design interfaces that enable
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participation in top international conferences to present your work Participate in yearly retreats organized by the doctoral network participants Support the dissemination of software tools and concepts Supervise
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academic and industrial cooperation partners Requirements: excellent university degree (M.Sc., Diploma) in either computer engineering, computer science, electrical engineering or any related natural science