10 parallel-programming "https:" "IMOST UMR 1240 INSERM" Postdoctoral research jobs at KINGS COLLEGE LONDON
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20 Feb 2026 - 00:00 (UTC) Country United Kingdom Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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United Kingdom Application Deadline 4 Jan 2026 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description About us The
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Researcher (R3) Country United Kingdom Application Deadline 11 Jan 2026 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a
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) Country United Kingdom Application Deadline 19 Jan 2026 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme
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recognised scientists and clinicians from across the School and King’s College London. More information: https://www.kcl.ac.uk/scmms About The Role This is an exciting opportunity for a Postdoctoral Research
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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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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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-world data, with strong programming proficiency in R or Python and version control systems like Git. Familiarity with spatial and statistical libraries (e.g. INLA, PyMC, scikit-learn, GeoPandas). Proven
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-world data, with strong programming proficiency in R or Python and version control systems like Git. Familiarity with spatial and statistical libraries (e.g. INLA, PyMC, scikit-learn, GeoPandas). Proven