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networks using common Python-ML libraries such as PyTorch preferably also background knowledge in computational mechanics and applied mathematics highly proficient in spoken and written English We offer you
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education, research, and innovation, which has been inspiring generations of thinkers for over 400 years. Novel optical access and metro networks are essential to support fast growing capacity requirement
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for molecular system. Recently, we reported a major breakthrough in using chemical reaction networks for so-called in chemico reservoir computing (Nature, 2024, 631, 549–555). This work demonstrated that self
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supporting the Net Zero 2050 target. This PhD project will develop an AI-enabled framework that optimizes wind turbine control and predictive maintenance. Using Deep Reinforcement Learning (DRL), the system
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PhD: Recognizing Multicultural Strengths of Youth via Social Networks at Work Faculty: Faculty of Social and Behavioural Sciences Department: Social Sciences Hours per week: 36 to 40 Application
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. The position is placed in the Division for Computer Networks and Systems and is formally employed by Chalmers University of Technology. Our research spans from theoretical computer science to applied systems
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: Computational Modelling: Employing simulation tools (e.g., GEANT4, light transport) to explore novel metamaterial designs, predict performance, and optimise key parameters such as timing resolution, light yield
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to improve structural durability, reduce material consumption, and support the UK’s net-zero goals. Funding notes: The position includes a full scholarship for UK-based PhD candidates and a half-scholarship
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for: computers able to process information more like the brain, studying how to reproduce some of the properties of biological neurons and synapses using networks of molecules and nanostructures, and other
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effort at the intersection of machine learning and applied mechanics. The focus of this position is on extracting information about what a neural network has learnt in a symbolic and (human) interpretable