31 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at University of London in United Kingdom
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for medicine use before and during pregnancy. This postholder would work primarily on a recently funded programme of work to develop a novel approach to understanding and communicating the Safety of Medicines in
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help supervise associated PhD students. The successful candidates will join large, supportive research teams led by Profs Knight, Screen and Connelly all working collaboratively at Queen Mary. This is an
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will plan and conduct experiments, generate high-quality data, prepare publications, make presentations and help supervise associated PhD students. The successful candidates will join large, supportive
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About the Role The combination of personalised biophysical models and deep learning techniques with a digital twin approach has the potential to generate new treatments for cardiac diseases. Our
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PhD (or close to completion) or research qualification/experience equivalent to PhD level in the relevant subject area for the research programme; with a productive track record and have experience
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haemostatic responses in ageing individuals. About You We seek outstanding, self-motivated, and ambitious junior researchers committed to pursuing a scientific career. Demonstrable knowledge and a PhD (or close
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, Spain and Norway. The project runs until early 2028 and investigates the potential role of performance-based arts in understanding how coastal communities learn about and respond to ecological crises
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and Immigration website . Full-Time, Fixed-Term (36 months) We are looking for a highly motivated early career researcher with a PhD (or near completion) in psychology, life sciences, genetics
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About the Role We are looking for a Postdoctoral Research Assistant to work with Dr Chema Martin on a Human Frontiers Science Program Research Grant project entitled “Evolutionary Biophysics
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related to gravitational wave astronomy. The primary aim will be the development of advanced approaches for computational Bayesian Inference to measure the properties of Compact Binary Coalescence signals