899 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" uni jobs at Nature Careers
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water matrices and case studies, while the AI4Science PhD will develop machine‑learning models that learn from and build upon these pNTA results. The successful candidate will be supervised by Prof. Dr
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but not required: Experience or interest in machine learning and artificial intelligence Experience in survey research and study design Salary range of this position is 140K to 200K and reflects
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of artificial intelligence (AI) and biomedical engineering. Research directions include deep learning, natural language processing, brain–computer interfaces, and their applications in disease prediction, drug
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of emerging methods in metabolic analysis, metabolic modelling, machine learning, and data-driven biology, identifying opportunities to apply new tools to accelerate discovery. Work closely with experimental
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: Joining an energetic, intellectually vibrant, and collegial lab team. Opportunities to learn, grow as an administrative professional, and be mentored. Joining the vibrant University of Washington research
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this but have hitherto struggled with tackling such challenging systems. With the emergence of machine learning methods in the physical sciences, things are rapidly changing. This project is part of a
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quality of care in hospitalized or critically ill patients. Using physiologic monitoring devices and digitized patient data, we implement statistical and machine learning decision support tools to detect
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Usability evaluation Geographical and spatiotemporal data on environmental and socioeconomic context Ideal expertise/experience includes: Advanced machine learning (deep learning, representation learning
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outstanding researchers with a strong track record of advancing the state of the art in machine learning and potentially its application to biology and biomedicine, with the ambition to build an internationally
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(e.g. Nextflow) and cloud compute environments (e.g. OCI, AWS, GCP) Familiarity with Bayesian methods, machine learning, or causal inference in the context of biological data Contributions to open-source