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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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an advantage: Deep learning or machine learning Structural bioinformatics Protein structure prediction and modelling Molecular simulations Membrane proteins or membrane biophysics Scientific software development
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Science Research unit: https://genomics.iit.it/ ESSENTIAL REQUIREMENTS PhD in computational biology, machine learning, bioinformatics, physics or related fields; High proficiency level in programming
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experience and evidence against the published criteria. Ideal candidate: The successful candidate will have a PhD (awarded or expected within 6 months) in Computer Science, AI, Machine Learning, Computational
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 2 months ago
. The successful candidate will be expected to carry out the following scientific activities: Application of advanced Machine Learning, Deep Learning, reduced-order modelling, and physics-informed/-guided modelling
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actively on the preparation and defence of a PhD thesis in the field of explainable reinforcement learning (XRL). Explainable reinforcement learning aims to make decisions, policies, and learning processes
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actively on the preparation and defence of a PhD thesis in the field of continual reinforcement learning. Continual reinforcement learning studies how agents can learn across a sequence of changing tasks
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/artificial intelligence methods. The Research Associate will have significant experience in research into neurodegenerative disorders using speech and deep learning technologies. They will establish the design
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environment within the Physical Vision Group (PVG, https://physicalvision.github.io/ ). This will involve closely collaborating with faculty, postdoctoral researchers, and PhD students, bridging foundational
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deep learning has led to the emergence of Transformers-type networks whose performance has revolutionized the field. These networks are the basis of next-generation image processing models (such as