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contexts. They will be expected to contribute to the effective delivery of teaching, learning, and student support by: Delivering and evaluating classroom and practical teaching across modules Designing and
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, Nutritional Sciences and Women's Health cluster) for REF was rated as world-leading or internationally excellent. We use this expertise to teach the next generation of health professionals and research
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bioinformatic workflows. Familiarity with biomedical ontologies and text mining on Electronic Health Records and biomedical literature Knowledge of machine learning / deep learning with an interest in
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machine learning or AI methods in healthcare research, particularly within digital trials or real-world data studies. Expertise in analysing complex digital health data, including wearable sensor data
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, Bayesian Statistics with a focus on nonparametric methods, Bayesian Computational Methods, Extreme Value Theory, Biostatistics, Probabilistic machine learning, Medical sciences and engineering applications
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machine learning or AI methods in healthcare research, particularly within digital trials or real-world data studies. Expertise in analysing complex digital health data, including wearable sensor data
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postgraduate programmes to military and civil service students, responding to the ever-evolving needs of military education. Our teaching equips professionals with the knowledge, creativity and intellectual
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spatial transcriptomics and imaging genomics projects, integrating bulk and single-cell RNA-seq datasets, and applying advanced statistical and machine-learning methods (AI/ML) to extract novel biological
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machine learning or AI methods in healthcare research, particularly within digital trials or real-world data studies. Expertise in analysing complex digital health data, including wearable sensor data
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external). High level of computer literacy including familiarity with MS Office applications and some experience with content management systems (e.g., Contensis and/or WordPress) and learning management