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characteristics using hybrid machine learning approach. This project is in collaboration with Kyushu University, Japan under the supervision of Dr Amin Paykani at Queen Mary University of London to realise
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meetings across the EU, perform R&D in the field of machine learning for plant species classification and support the implementation of the project application development. About You Applicants must have a
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apply Overview About the Role Applications are invited for a post-doctoral research associate to lead on a Barts Charity funded project. The research involves application of machine learning methods
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for this position will have a PhD in physics/biophysics/mathematics/bioengineering /bioinformatics/computational science or similar field, including significant experience in programming. In particular, past
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Telescope. This research may involve the analysis of large datasets, the generation of new simulations and the utilisation of Machine Learning techniques. The PDRA will be expected to hold a PhD in
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Holloway, University of London. Successful applicants will be working under the direction of Dr. Matteo Sammartino on the EPSRC-funded "Verification of Hardware Concurrency via Model Learning" (CLeVer
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imaging, electrocardiogram (ECG) and electronic health record (EHR) data to improve diagnosis, management and outcomes of heart failure. About You You will have either a PhD in the relevant subject (e.g. in
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science for the environment and sustainability. The role is funded for 24 months in the first instance, with an expected start date of July 2024. About You You will have a PhD and track record in either
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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