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to the table A university degree in computer science, IT security, or a related discipline Knowledge of IT security / software security Sound programming skills in Java and/or C/C++ are also needed in larger
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Informatics. You are passionate about applying cutting-edge information technology to solve the energy and climate crisis and would like to work in a vibrant and international research environment? Then let’s
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position, you will have the appropriate qualifications. Please send us a meaningful CV and references. motivation, initiative, flexibility, and willingness to perform a strong independence and reliability as
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. These EMS coordinate distributed renewable generation like solar and wind, flexible loads like heat pumps and electric vehicles, and distributed energy storage like stationary batteries and hydrogen
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of e.g. proteomics, lipidomics, or metabolomics data Requirements: Candidates must hold a master’s degree in Data Engineering, Data Science, Bioinformatics, Informatics, or a related discipline. Essential
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dynamic team. As part of the newly funded ERC Synergy consortium EPIC, you will develop scalable and robust software to train and apply AI models for regulatory genomics. About us The Chair of Computational
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more than 12 months in the 36 months immediately before the recruitment date. Nationality: Open to all nationalities. Doctoral Programme Enrolment: You must be willing to enrol in a doctoral programme at the
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. The project is jointly supervised by Prof. Dr. Ralf Jänicke (Institute of Applied Mechanics, Technische Universität Braunschweig) and PD Dr. Stefan Kollmannsberger (Chair of Computational Modeling and
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studying mechanical engineering, computer science, industrial engineering or a comparable subject Experience in the field of machine learning is an advantage A high degree of initiative and team spirit Very
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Learning algorithms for EDA in terms of Computational Complexity, Performance Scores, etc. To learn more about our previous work, please check out our website ( www.cda.cit.tum.de/research/machine_learning