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Max Planck Institute for Security and Privacy, Bochum | Bochum, Nordrhein Westfalen | Germany | 2 months ago
Job Code: history of cryptography Job Offer from February 05, 2026 The Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany, is offering a position for one Postdoctoral Scholar
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functional materials toward energy/electronic devices that deliver reliable performance under demanding, real-world conditions. The group is led by Prof. Husam N. Alshareef (Ibn Alhaytham Distinguished
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operando data related to battery degradation and safety. You will develop and implement advanced deep learning models to analyse multi-modal operando data from accelerated stress testing, with the aim
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leader Prof. Hiroaki Kikuchi, Meiji University) aims to develop innovative data privacy technologies. A post-doctoral researcher who engages in the following studies is now open. https
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, Teamwork, Safety, and Service Basic Qualifications: A Ph.D. degree in Mathematics, Computer Science, Physics, Chemistry, Engineering, or a related discipline. A strong foundation in linear algebra (tensor
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degradation, and connect them to cell- and pack-level safety performance. · Design model-based and data-driven frameworks for battery management systems, including health monitoring, early fault detection, and
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optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos. Become a part of our team and join us on our journey of research and innovation! Be
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The Quantum Information and Computation group in the Mathematical Institute, led by Prof Balint Koczor, is looking for a highly motivated and exceptionally talented quantum theorist to lead new
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Location: Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG Contract type: Fixed Term until 30th September 2028 The Quantum Information and Computation group in
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The Role The role focuses on advancing research in explainable and trustworthy machine learning, with a particular emphasis on mechanistic interpretability and its application to healthcare data