36 coding-"https:"-"https:"-"https:"-"https:"-"INSA-toulouse" Postdoctoral positions in United Kingdom
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accretion-powered outflows, using hydrodynamics codes, theoretical calculations and/or the Monte Carlo radiative transfer code SIROCCO. The postholder will carry out research involving theoretical and
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of accelerator physics modelling codes such as Accelerator Toolbox or ELEGANT would be an advantage. The post-holder will have the opportunity to teach. This may include lecturing, small group teaching, and
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-in-cell computer codes hosted on local and national high-performance computing clusters; establishing all-optical diagnostics to map temperature evolution in plasma accelerators; exploring novel inter
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proof-of-principle repetition-rate and staging experimentation. The successful candidate will perform duties that include developing/using particle-in-cell computer codes hosted on local and national high
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findings, and help attract funding. PhD in Computer Science, Computational Bioengineering, Mechanical or Electrical Engineering Excellent coding skills Preferably a familiarity with machine learning and deep
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may be sought in advance of interview but only with the permission of the candidate. Confirmation of your right to live and work in the UK. *Non UK applicants can apply. Where a share code is provided
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Constrained experimental design Combining models and combining data / Realistic simulation of clinical trials Developing LLMs to utilise ODEs and ProbML as tools, Code synthesis for causality Generalisability
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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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Full time, fixed term until (31/10/26) Closing Date: 23:59 on 5th December 2025 Interview Date: 15 December 2025 By reference to the applicable SOC code for this role, sponsorship may be possible
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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