158 data-"https:" "https:" "https:" "https:" Postdoctoral research jobs in United Kingdom
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for applications is 12.00 noon on Friday 06 March 2026. Informal enquiries may be addressed to Professor Harrison Steel: Harrison.steel@eng.ox.ac.uk Further information can be found at http://www.eng.ox.ac.uk/jobs
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at this grade. Appointments will always be made based on skills and experience aligned with the job description. For further information, please visit: http://www.chemistry.manchester.ac.uk/about-us/athena-swan
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holder will be responsible for the day-to-day running of this programme of work as part of Dr Chiari’s research team within the Cells, Organisms and Molecular Genetics (COMGen) group (https
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writing skills are essential. Additional information: Interviews are planned for 17th March 2026. For further information and details of how to apply, please visit our website http://jobs.jic.ac.uk
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: For further information and details of how to apply, please visit our website http://jobs.jic.ac.uk or contact the Human Resources team on 01603 450814 or nbi.recruitment@nbi.ac.uk quoting reference 1006046
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of society, particularly from underrepresented groups. For more information, please see our website https://www.hw.ac.uk/uk/services/equality-diversity.htm and also our award-winning work in Disability
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Months Rewards and Benefits: 33 days annual leave, plus 9 buildings closed days for all full time staff (Part time workers should pro rata this by their FTE). Use our total rewards calculator: https
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qualified women to apply. Given equal qualifications, preference will be given to female candidates. University of Vienna. Space for personalities. Since 1365. Data protection Application deadline: 02/23
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funded by EPSRC. The project will be led by Carmine Ventre, https://www.kcl.ac.uk/people/carmine-ventre . The role holder will join the Distributed AI group in the Department of Informatics at King’s
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
for sequential data modelling, including Physics-informed Machine Learning and Koopman Operator-based representation framework, towards building interpretable predictive models for complex multi-physics dynamical