116 phd-computational-"IMPRS-ML"-"IMPRS-ML"-"IMPRS-ML" positions at Forschungszentrum Jülich
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Infrastructure? No Offer Description Work group: IAS-9 - Materials Data Science and Informatics Area of research: Promotion Job description: Your Job: The PhD project is methodologically independent, with
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Your Job: The PhD project is methodologically independent, with the opportunity to contribute to collaborative efforts at the interface of data science, imaging, and materials research. You will
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(UTC) Type of Contract To be defined Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research
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energy system model workflows Your Profile: Master’s degree in computer science, data science, natural sciences, economics, engineering, mathematics or a related field of study Huge interest in data
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Aachen. ENROLLMENT: The student will be enrolled in the PhD program of the department of Electrical Engineering and Information Technology, RWTH Aachen LEAVE: You will receive 30 days of leave plus
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of Electrical Engineering and Information Technology at RWTH Aachen. ENROLLMENT: The student will be enrolled in the PhD program of the department of Electrical Engineering and Information Technology, RWTH Aachen
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of event cameras, with the goal of achieving accuracy, efficiency, and real-time performance on robotic platforms powered by edge-computing hardware. In addition, knowledge of Model Predictive Control (MPC
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enrolled in the PhD program of the department of Electrical Engineering and Information Technology, RWTH Aachen. Targeted services for international employees, e.g. through our International Advisory Service
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for Doctoral Researchers and Supervisors: https://www.fz-juelich.de/en/judocs You will be enrolled in the PhD program of the department of Electrical Engineering and Information Technology, RWTH Aachen. Targeted
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degree (or equivalent) in Data Science, Computational Biology, Bioinformatics, Computer Science, Physics or a related field Solid programming skills and knowledge in deep learning, statistical modelling