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Job related to staff position within a Research Infrastructure? No Offer Description Join a transformative project at RIBES using machine learning and genomics to overcome linkage drag and accelerate
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-analysis, regression modelling, machine learning). You have a solid basis in at least one common high-level programming language (e.g. R, Python). You enjoy collaborative research in international
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join us as a PhD candidate. You will work in a highly interdisciplinary group, at the intersection of physics, machine learning and theoretical neuroscience. Our group is focused on investigating
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? You hold a PhD degree in ecology or environmental science. You have proven experience with quantitative analytical methods (meta-analysis, regression modelling, machine learning). You have a solid basis
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recent advances in machine learning and browser automation, the project aims to provide tools, techniques and datasets to effectively address these threats. As a PhD candidate, you will play a key role in
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challenges, creating synergy and fostering new collaborations. For example, one project might involve developing machine learning models to analyse complex neuroimaging data. Another could focus
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electron ground states. Another promising route towards physical implementations of energy-based machine learning and neuromorphic hardware is to utilise material platforms that exhibit multiwell behaviour
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or computational neuroscience/machine learning. You possess solid programming and software engineering skills. You have excellent written and spoken English skills. You are a proactive team player, who enjoys
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goal is to apply cutting-edge neuro-AI knowledge to various scientific challenges, creating synergy and fostering new collaborations. For example, one project might involve developing machine learning
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-ACCENT project aims to fill this gap by combining insights from cognitive psychology, neuroscience, AI engineering, human-computer interaction and social science, with lifespan perspectives. Using advanced