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Field
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numerical simulations and machine learning in order to better understand and characterize active matter systems. A possible direction is to use physics-informed machine learning techniques to connect
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on futuristic technologies in the field of machine learning and computer vision. Hence, we investigate and develop an innovative computation-in-memory (CIM) solution for artificial intelligence accelerator design
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after deployment, leveraging recent advances in physical reservoir computing, contrastive learning, and biological decision-making paradigms. You will: - Develop and apply decentralized learning
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, enabling Digital Twins and AI-based decision support for design and process choices. This full-time position offers access to advanced laboratories, simulation environments, and a multidisciplinary research
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the job funded through the EU Research Framework Programme? Horizon Europe - MSCA Marie Curie Grant Agreement Number 101311637 Is the Job related to staff position within a Research Infrastructure? No
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funded through the EU Research Framework Programme? European Union / Next Generation EU Reference Number 2026/CP/228 Is the Job related to staff position within a Research Infrastructure? No Offer
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the job funded through the EU Research Framework Programme? Horizon Europe - MSCA Marie Curie Grant Agreement Number 101311637 Is the Job related to staff position within a Research Infrastructure? No
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Temporary Job Status Full-time Hours Per Week 40 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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industrial partners. In particular, you will: Develop and validate physics-based, data-driven, or hybrid digital twins of Electrolysis systems, capturing their electrochemical, thermal, flow, and system-level
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, plus preparing modelling outputs that can be shared with project’s partners. The successful candidate will work with academic and industrial project partners to generate simulation and experimental