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, simulations, AI, and machine learning applied to proteins. A track record of research outputs, including publications and presentations at national or international level. Excellent communication and
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geometry, and/or data science. Specific topics of focus include, but are not limited to, linear response, random and nonautonomous dynamical systems, spectral analysis, machine learning, data-driven dynamics
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, and a PhD research program with over 120 students. 40 academic staff conduct experimental research in many areas of Psychology, including behavioural and cognitive neuroscience, perception, learning
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agricultural science with a quantitative focus (or an equivalent discipline) expertise in statistical and machine learning approaches, with the ability to apply advanced methods to complex environmental and
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, algorithmic methods, and machine learning approaches to advance research in melanoma and cancer biology. Specifically, you will support the major project “Predicting Early-Stage Melanoma at High Risk of
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Intelligence or Machine Learning, with demonstrable analytical skills. Excellent research record evidenced by first-author publications in strong international journals and conferences. Proven experience in
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. Experience in molecular modelling, simulations, AI, and machine learning applied to proteins. A track record of research outputs, including publications and presentations at national or international level
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(e.g., Docker, Kubernetes, cloud/edge environments). Demonstrated expertise in AI, distributed computing, machine learning, or systems software design. Strong background in software engineering
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Academic Level B: Completion of a PhD in the field of Computer Science/Artificial Intelligence. Software engineering expertise, including design and implementation of AI-based models (machine learning, deep
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individuals. iPSC “Village” systems and CRISPR perturbation to experimentally dissect and validate gene function in controlled, scalable cellular models. Advanced computational genomics, machine learning, and