11 engineering-computation-"https:"-"https:"-"https:"-"https:"-"DFG-TRR" Postdoctoral positions at KINGS COLLEGE LONDON
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are not limited to: natural language processing, large language models, graph learning, prompt engineering, knowledge graphs, knowledge engineering, linked data, web technologies. About the role
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engineering, linked data, web technologies. About the role: The successful candidate will join the Distributed AI (DAI) group in the Department of Informatics, King’s College London. They will carry out
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and experience: Essential criteria PhD in applied mathematics, statistics, engineering, computational biology, econometrics, or a related discipline. Experience in developing complex models using real
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About us Our contemporary Chemistry Department, based in the Faculty of Natural, Mathematical and Engineering Sciences, provides an exciting place to explore the boundaries of the subject. The
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and experience: Essential criteria PhD in applied mathematics, statistics, engineering, computational biology, econometrics, or a related discipline. Experience in developing complex models using real
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geography/remote sensing, ecology, statistics, engineering, quantitative social sciences, or a related discipline. Experience in developing models and mapping with real world data, with strong programming
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& technology studies, development studies); Strong research profile for career stage, as demonstrated through lead authorship of well-placed publications; Excellent interpersonal and communication skills
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. The successful candidate will work within a multidisciplinary team to unravel the metabolic drivers of HCC biology and transplant rejection through cutting-edge spatial multi-omics and computational metabolic
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‘Work, Welfare Reform and Mental Health’ programme. This involves collaborating closely with an interdisciplinary team of researchers as well as the Centre’s academic and community partners, as part of
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research programme takes a mixed methods approach. The successful applicant will have a key role in the qualitative work package which aims to understand multi-stakeholder perspectives of the C(E)TR process