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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 2 months ago
(or surrogate models) are approximations of classical numerical solvers with a very low computational cost. They form the core of a digital twin. Using machine learning techniques to build these meta-models
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, Physics, Electrical Engineering, Communication Engineering, or equivalents; – Knowledge in artificial intelligence, statistical and machine learning, complex systems, agent-based modeling and simulation
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process data locally while ensuring efficient and scalable artificial intelligence at the edge. TinyML and Edge AI have demonstrated the feasibility of embedding machine learning models on such devices
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, relating to craniofacial identification research and machine learning. You will require a computer science background. You will be applying AI and/or machine learning to Face Lab processes in relation
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. The research will involve training machine-learning models on large structure and sequence datasets and integrating membrane-specific biophysical constraints to enable the design of membrane proteins and
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fundamental learning procedures to tackle distressing images related to aversive memories. The aim is to generate insights with direct impact on clinical practice and patient wellbeing. PhD Candidate Reducing
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important that you are able to: A good team player A good communicator Eager to learn Results-oriented Work independently Emphasis will be placed on personal qualities. We offer An exciting job with an
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stays at foreign educational institutions Support to education activities in courses within the Software Engineering area Other career-promoting work, such as learning grant preparation fundamentals Be
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with European industry. The EISLAB division at Luleå University of Technology conducts research in electronic systems design, sensor systems, cyber-physical systems, the Internet of Things and machine learning
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31.07.2026, Academic staff We are seeking a researcher in Scientific Machine Learning (SciML) to join the project "Data science at scale" at the Technical University of Munich, Germany. Ideal