10 programming-"https:"-"Inserm"-"FEMTO-ST"-"https:"-"https:"-"https:"-"BIP" PhD positions at University of East Anglia
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programs. The student will join a well-supported team of chemists and biochemists (Master’s and PhD students, and postdocs) who are well-placed to provide a supportive and ambitious peer group. The student
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@uea.ac.uk The Norwich Research Park Biosciences Doctoral Training Programme (NRPDTP) is offering fully funded studentships for October 2026 entry. The programme offers postgraduates the opportunity
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through The Lupus Trust. Training programme: Evidence synthesis, qualitative methods and analysis, mixed methods, statistical analysis potentially including meta-analysis, intensive longitudinal methods
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programming skills in Python/MATLAB, and an interest in digital twin technologies, cybersecurity and machine learning. Entry Requirements Acceptable first degree: Computer Science or related disciplines
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cell and spectroscopic analysers. Programming (e.g., R, Python) and machine learning for advanced atmospheric time-series analyses. Skills for presenting research at conferences and writing peer-reviewed
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interdisciplinary research skills in statistical analysis, data visualisation, advanced programming, writing and oral presentation, and receive training to enhance transferable skills and employability. Person
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to oceanography and climate research communities. PERSON SPECIFICATION This project is suited for a candidate with a background in natural sciences, engineering or mathematics, with good numerical and programming
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someone with previous experience in programming, and an interest in biogeochemistry, but do not necessarily require environmental science as a first degree. Previous laboratory experience is desirable but
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strong numerate background. Prior experience of mathematical or statistical programming is highly desirable. Informal enquiries concerning the project are welcomed by the primary supervisor. Entry
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measurement; Measurement of related tracers (e.g., Radon); Programming (e.g., R, Python) for advanced atmospheric time-series analyses, including machine learning; Skills for presenting research at scientific