433 data-"https:"-"https:"-"https:"-"https:"-"https:"-"RMIT-University" positions at University of Oxford
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Data Science Community, aiming to bring together researchers, data scientists, bioinformaticians, technicians, clinicians and more across the UK and beyond who are interested in or using data-driven
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, proteomic, clinical and experimental data for the study cohort. This is a full time post (35 hours per week), and you will be offered a fixed term contract until 31/03/2028. About You To be successful in
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health and behaviour throughout pregnancy, enabling a holistic understanding of health trajectories and personalised interventions. The post focuses on analysing multimodal data collected from wearable
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to someone wishing to embark on a career path focused on clinical trials. You will assist in specialised clinical data management within the unit’s Data Management Team. The primary responsibility is
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well as for overseeing the processes of sample and data collection, storage and distribution for research. You will operate in a complex and highly regulated research environment, ensuring that the OBB adheres
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will include coordinating multiple aspects of the project, refining working hypotheses in light of new data, and contributing ideas for new research directions. The post-holder will be encouraged
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to developers, and maintain well-organised project data. You will also assist in organising international workshops and contribute to project reporting, working within an interdisciplinary team. This is a full
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while contributing conceptually to the overall research programme. This will include coordinating multiple aspects of the project, refining working hypotheses in light of new data, and contributing ideas
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this role. You will contribute to the design of research materials and make arrangements for data gathering, including data from interviews and surveys, while analysing and presenting qualitative and
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operando data related to battery degradation and safety. You will develop and implement advanced deep learning models to analyse multi-modal operando data from accelerated stress testing, with the aim