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renewable energy processes, and want to engage in research with skilled colleagues who share your interests? Maybe you can become our new colleague at the Department of Process, Energy and Environmental
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8 Aug 2026 Job Information Organisation/Company GSI Helmholtzzentrum für Schwerionenforschung Research Field Physics Researcher Profile Recognised Researcher (R2) Established Researcher (R3
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About the project: Machine learning accelerated electronic transport calculations for complex materials Supervisor: Prof. Neophytos Neophytou, University of Warwick Advancements in materials
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application process here. About the position Are you motivated to develop next-generation research infrastructure at the intersection of robotics, mechatronics, sensor systems, and life science technology? We
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share your interests? Maybe you can become our new colleague at the Department of Process, Energy and Environmental Technology at The Faculty of Technology, Natural Sciences and Maritime Sciences
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 2 months ago
validation in a waste-sorting application, supported by PEPR Robotique Acceleration program. The PhD candidate will benefit from a rich scientific environment offered through the collaboration between INRIA
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physically realistic datasets for AI training, benchmarking, and validation. The successful candidate will contribute to AI.Grids, a flagship European initiative on the application of Artificial Intelligence
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and 43,000 students work to create knowledge for a better world. You will find more information about working at NTNU and the application process here. ... (Video unable to load from YouTube. Accept
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2 PhD Positions in Computational Modelling of Biological Membranes and Adaptive Functional Materials
of neuromorphic functionality. The primary goal is to identify the physical mechanisms underlying adaptive behaviour and to establish predictive theoretical models. Surrogate models may be employed to accelerate
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-accelerated, for self-aware neuromorphic system-on-chip (SoC) architectures, enabling fast and accurate exploration of emerging hardware and learning paradigms. The framework will support rapid prototyping