144 phd-studenship-in-computer-vision-and-machine-learning PhD positions at Nature Careers
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I-2503 – PHD IN EXPLAINABLE AI FOR DATA-DRIVEN PHYSIOLOGICAL AND BEHAVIORAL MODELLING OF CAR DRIVERS
Master’s degree or Engineer diploma in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or a related field. Experience and skills · Strong knowledge of AI, Machine Learning
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interest in user-centered security, human factors in security, human computer-interaction or UX. Academic: A Master in Cybersecurity or equivalent degrees with expertise in cybersecurity assessment or a
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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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integrating machine learning and domain-specific knowledge to predict failure arising from hydrogen embrittlement. You will carry out materials testing, computational model development, data processing, and
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of the convergence between nano, bio and ICT. The vision promoted at IBEC is to exploit and connect the multidisciplinarity of its groups, aligning their complementary capacities through four broad areas of expertise
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that are commonly applied to learning and teaching practice in computer science education. Employment Terms The PhD student is expected to teach FNUG’s courses MM107 Dynamic Systems and Interdisciplinary Subject
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Bioinformatics, Computational Biology, Computer Science, Biomedical Engineering, Computer Engineering, Genetics/Genomics or related field experience with ‘omics platform output experience with biological datasets
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PhD Candidates Graduate School of Frontier Science Initiative, Kanazawa University is seeking candidates for PhD students in the fields of Lifescience and Biotechnology, Neuroscience, Chemistry and
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into University of Galway’s community. Living allowance (Stipend): €25,000 per annum [tax-exempt scholarship award]. Computer equipment and funding for travel (e.g. to conferences) as well as attendance
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patient clusters and digital phenotypes, leveraging machine learning approaches to identify individuals at high CV risk based on clinical and biochemical markers, immune markers, digital health data (e.g