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automated liquid handling systems; and interest in machine learning and AI. 3/23/2026
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engineering applications at the BEng, BSc, MSc, and PhD levels. In addition, there will be an obligation in continuous education on advanced machine learning methods and AI. The Section for Cognitive Systems
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functional theory. - Effective Hamiltonian methods for quantum phenomena in solids. - Development of machine learning tools for topological materials. - Experimental studies of magnetotransport in quantum
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of: • machine learning • cybersecurity • distributed systems • privacy-enhancing technologies The research will be carried out within the (team name) at LS2N, focusing on trustworthy AI and cybersecurity
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or more of the following areas: AI and machine learning, natural language processing, large language models (LLM), experience in designing prompts, fine-tuning LLMs, or distributed systems. Good knowledge
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and adapt machine learning and deep learning models (e.g., convolutional and transformer-based architectures) to biological questions in collaboration with investigators. Develop interpretable models
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, energy consumption, and packet loss. The use of distributed machine learning provides a relevant solution to mitigate the lack of communication reliability [3][4]. This PhD proposes to guide the learning
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of senior researchers, the individual will help apply machine learning methods, with a focus on reinforcement learning, to mathematical problem solving. The role emphasizes hands-on experimentation
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Research and Imaging Centre (BRIC; https://www.plymouth.ac.uk/research/psychology/brain-research-and-imaging-centre ) . TARAs benefit from waived study fees and will work towards a PhD in an area of
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, computational neuroscience, bioinformatics, robotics, or a related field Strong expertise in computational data analysis (e.g., behavioral analysis, signal processing, or machine learning) Experience working with