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Doctoral Candidate in computer vision and machine learning for developing novel deep learning method
Machine Learning (DM3L) Doctoral Candidate in computer vision and machine learning for developing novel deep learning methods for satellite-based tracking of global CO2 and NOX emissions of point sources 80
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applying machine learning to a large and diverse, curated clinical dataset. The candidate should have a PhD or MSc in a relevant field such as Neuroscience, Cardiovascular Science, Computer
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Development and Validation of a Multimodal Wearable Headband for Objective Bruxism Monitoring Using Machine Learning (S3.5-DEN-Boissonade) School of Clinical Dentistry PhD Research Project
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Machine Learning Quantum Phase Transitions via Topology- and Symmetry-aware AI School of Mathematical and Physical Sciences PhD Research Project Competition Funded UK Students Dr Marco Fazzi
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theater and/or performance studies and who can teach broadly at the university level in theater and/or performance studies. Applicants must have completed all requirements for receipt of a PhD, DFA, MFA, or
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PhD in Computer Science / AI / Machine Learning / NLP or a closely related area) or equivalent qualifications. Extensive experience and scholarship within subject specialism. Ability to design, deliver
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mathematical statistics, machine learning, coding, and algorithmics. Contribute to programme design, accreditation, and innovative teaching strategies that enhance student experience and employability. Research
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well as applications of multi-level modeling, machine learning, and/or AI for clinical prediction and estimation of novel dynamic phenotypes related to brain health and disease risk. The Program Director will report to
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biologically trained investigator to expand their analytical skills, implement new methods in machine learning, and to understand the roles of the neuromodulator’s dopamine, serotonin and norepinephrine in human
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effectiveness and safety. This PhD project aims to address this challenge through biomimetic engineering design, combining predictive in silico modelling with machine-learning techniques and microfluidic