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contribute to research on applied machine learning methods that support robust decision-making under uncertainty. This may involve working with structured datasets, panel data, text data, regulatory documents
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Applications are invited for a Postdoctoral Research Associate in transportation data science and statistical modelling, based in the Centre for Transport Engineering and Modelling (CTEM
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as well as MSc and PhD programmes. Further information may be found at: http://www.kcl.ac.uk/physics About the role: We are seeking a highly motivated and experienced researcher, holding a PhD in
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demonstrate equivalent research experience and qualifications. Experience in both wet-lab and dry-lab research is beneficial. You must have expertise in analysis of large-scale genomic data and confidence in R
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publications or submitted manuscripts in referred journals* Strong evidence of data presentation at scientific meetings Strong organisational skills and thorough record-keeping Ability to work independently and
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. Additional Information Closing date for all applications: 23 August 2026 at 11:59pm Interviews are scheduled to take place during the week commencing 7 September 2026. For more information please refer to
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to an inclusive research environment. Additional Information Find more info on our career site Closing date for all applications: 23 August 2026 at 11:59pm Interviews are scheduled to take place on week commencing
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for Research Staff Development for more information. About you To be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD qualified in
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of physical and chemical characterisation/testing of products. Data collection, interpretation and professional report writing skills are required. For an informal discussion about the post please contact Prof
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. The role is designed to strengthen research capacity in empirical, data-driven analysis with a particular focus on generating robust evidence that challenges prevailing assumptions and dominant narratives