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is Prof Cally Tann. We are seeking a Research Fellow in Early Child Development & Disability to coordinate the development, implementation and evaluation of the programme, including: mixed methods
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will deliver projects that leverage large-scale electronic health record data and rich cytometry data derived from full blood count analysers to develop and refine machine learning models to improved
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their knowledge in this area. This may include developing autonomous electromagnetic warfare systems to evaluate the effectiveness under hypothetical electromagnetic defence and offence strategies under uncertainty
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project on Addressing socio-technical limitations of Large Language Models (LLMs), particularly for medical and social computing (https://adsolve.github.io/ ). The role involves developing methods
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and development of the research programme. The successful candidate will undertake the research investigations under the supervision of the Principal Investigators and in collaboration with other
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(QMUL). The researcher will be working under the supervision of Prof. Matteo Palma (QMUL): see http://research.sbcs.qmul.ac.uk/m.palma/. We have developed different nanohybrids platforms interfacing low
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including cell culture, organ-chip models, tissue engineering, and musculoskeletal biology. The PDRA will plan and conduct experiments, generate high-quality data, prepare publications, make presentations and
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project developing Bayesian causal inference methods for mediation analysis using Electronic Health Records (EHR) data. The Research Fellow will design and implement Bayesian methods and software
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to work with an international team on developing cutting-edge novel demographic, statistical and computational methods in estimating, modelling and forecasting measures of health, well-being, and human
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exciting project that will develop new approaches to handle missing data in statistical analyses based on machine learning methods. The Research Fellow will be based in the Department of Medical Statistics