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and citizen action. They will also contribute into teaching in the interdisciplinary Creative Sustainability master’s program, and preparation of research funding applications in the NODUS Sustainable
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on Experimental Study and Computational Solids Mechanics model of soft multifunctional materials such as magneto-active polymers (MAPs), Electro-active polymers (EAPs). The successful candidate will work on the
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computer codes to solve some of the daily research problems and have experience with high performance computing. You should have a PhD in Chemistry, Physics or Materials Science with a proven research track
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neuroscience or a related field. Experience of MEG/OPM-MEG acquisition and data analysis is essential. Experience with MATLAB or Python computer programming for neuroimaging data analysis is essential
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Full time: 25 Hours per week Fixed term: 12 months We are looking for a candidate to join the University of Edinburgh to conduct research on Machine Learning, Reinforcement Learning, or LLM orchestration, with applications to Finance. This post is advertised as full-time (35 hours per week). We...
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) comprises Chemistry, Engineering, Informatics, Mathematics, and Physics – all departments highly rated in research activities and a wide-ranging portfolio of education programmes. Celebrating diversity and
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explores novel aggregation methods at the intersection of AI safety, computational social choice, and judgment aggregation, aiming to formally integrate multi-stakeholder preferences into AI system design
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essential. Experience with MATLAB or Python computer programming for neuroimaging data analysis is essential. Experience in MRI and/or EEG acquisition and analysis is desirable. Experience working on research
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researchers and PhD students, pursuing research in many aspects of field theory, string theory, and integrability (for more information, see www.hw.ac.uk/uk/schools/mathematical-computer-sciences/research/maths
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skills and knowledge of biodiversity to plan and lead research on how biodiversity change is unfolding in the Anthropocene. This project will assess and analyse existing biodiversity and ecological data