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to several microns. To cover this, we have supplemented the existing setup at I22 with a Bonse-Hart ultra-small angle x-ray scattering instrument. We now have an opportunity for a Post-Doctoral Research
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that form our core research programme. They will be a cornerstone of the centre, collaborating across our three Research Pillars to generate hypotheses and data to underpin subsequent grant applications and
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the universities of Manchester and Oxford. The post-holder will be one of six centre-funded postdoctoral researchers delivering on projects that form our core research programme. They will be a cornerstone of the
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United Kingdom Application Deadline 5 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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) Country United Kingdom Application Deadline 15 Dec 2025 - 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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or clinical domains. Strong knowledge of MRI data formats (DICOM, NIfTI) and image preprocessing tools (e.g., MONAI, SimpleITK). Excellent programming skills, demonstrated through available code or projects
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Assistance Programme Exceptional starting annual leave entitlement, plus bank holidays Additional paid closure over the Christmas period Local and national discounts at a range of major retailers As an equal
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About us A post-doctoral research associate position is available at the Photonics & Nanotechnology group, Physics Department, King’s College London, funded by the EPSRC Programme Grant Next
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Application Deadline 30 Nov 2025 - 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 related to staff
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formats (DICOM, NIfTI) and image preprocessing tools (e.g., MONAI, SimpleITK). Excellent programming skills, demonstrated through available code or projects, with proficiency in Python and deep learning