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machine learning and AI acceleration. Perform performance, power, and area (PPA) analysis of processor and accelerator designs. Publish research findings in top-tier conferences and journals and contribute
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to recruit faculty in biomedical informatics with expertise in artificial intelligence and machine learning (AI/ML) for tenure-track and tenured positions in the School of Medicine at the University
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background in the topics related to this PhD position. Good knowledge of combinatorial optimization (scheduling problems, mathematical modelling etc.), machine learning and/or strong programming skills
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students who are prepared for a lifetime of learning and rewarding work. Candidates should hold a PhD or master’s degree in electrical and computer engineering or related fields and should be comfortable
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accelerated AI, machine learning, and robotics algorithms with a strong focus on computational efficiency, memory reduction, and energy-aware deployment. The role targets foundation models, including large
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health, epidemiology, statistics, biostatistics, or machine learning/artificial intelligence. You must have a strong academic background from your previous studies and have an average grade from your
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machine learning and computer simulations. The focus of the PhD project will lie on developing machine learning models for clustering, classification, regression and reinforcement tasks to work with
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motivated to acquire new skills. Candidates must be fluent in English and/or French with scientific writing skills. The doctoral contract will take place at the CRISMAT laboratory (https://crismat.cnrs.fr
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-learning–based segmentation, species classification and lineage tracking workflows for multi-species time-lapse data Optimise models and pipelines for real-time performance, enabling adaptive imaging and
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Economics, Mathematical and Computational Biology, Theoretical Ecology), Statistics, Machine Learning and Data Science, and Theoretical Computer Science are especially encouraged to apply. The School has