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. Applicants at the PDRA level must have a PhD in Computer Science or a related field. Expertise in intelligent decision-making under uncertainty is essential, relevant areas include influence diagrams, causal
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at the PDRA level must have a PhD in Computer Science or a related field. Candidates must have substantial knowledge in Deep/Machine Learning, Large Language Models (LLMs) Natural Language Processing and
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Science/Electronic Engineering (or equivalent). Applicants at the PDRA level must have a PhD in Computer Science or related field. Candidates must have substantial knowledge in Deep/Machine Learning, Audio and/or
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Grade 5, Spinal Point 31 [£42,405 per annum (pro-rata)], inclusive of London Allowance. Interviews will be held in late April or early May. About You Candidate will have a PhD relevant to mental health
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. The Research Assistant will also play a key role in supporting the development of the project through dissemination and funding. About You Candidates are expected to have a PhD (submitted, or near completion) in
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echocardiography and monitor whether the innovative therapeutic could attenuate the cardiomyopathy in this model. About You Applicants should ideally hold a PhD degree or equivalent in biological or related science
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medical contexts are highly regarded. Dissemination of findings through publication is an essential aspect of these positions. The selected candidate will collaborate as part of a cohesive unit to; 1
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training ambitions of the successful applicant. The successful candidate requires a PhD degree in a biological, medical or chemical sciences discipline, with a good track record in publishing their work in
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quality research publications. About You The successful candidate will have PhD degree in bioinformatics, or significant post-doctoral experience with single-cell machine-learning methodologies. In
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methods (Efremova et al. Nature Protocols, 2020; Jain et al., Genome Biology). About You Candidates should hold a PhD in a biological or computational subject and have background in computational analyses