412 web-programmer-developer-"https:"-"https:"-"https:"-"UCL" Postdoctoral positions in United Kingdom
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as soon as possible but must be available to start by 1 April 2026 at the latest. This project aims to develop superconducting microwave interconnects and metasurfaces for distributed quantum networks
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for a highly motivated and driven postdoctoral researcher to contribute strongly to a wave of ongoing developments deploying the escape-time technology on a broad range of measurement problems in biology
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numerical cosmology. The successful candidate will lead the develop of realistic simulations of a galaxy, lensing, X-ray and CMB secondary observables. This will be built on the backlight simulations, a new
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to their ongoing research programme, which aims to unravel the complex mechanisms underpinning 3-dimensional growth in plants. This is a fixed term position for one year. About you The successful applicant will hold
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scheduling. You publish internationally and give lectures. You apply for projects and raise third-party funds. You prepare and complete a publication-ready habilitation. You hold courses independelty within
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computational research, developing and optimising advanced cellular and molecular immunology assays. Techniques will include multi‑parameter (spectral) flow cytometry or mass cytometry, human cell isolation and
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specific domain (online and/or in-lab), ideally complemented by research experience in industry and familiarity with experimental data from economics and psychology. You will be confident developing multi
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are committed to support career development and encourage innovative thinking to tackle major challenges in Immunology. About the role We are seeking a highly motivated Postdoctoral Research Associate in
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are committed to support career development and encourage innovative thinking to tackle major challenges in Immunology. About the role We are seeking a highly motivated Postdoctoral Research Associate in
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turbines represented using an actuator-line approach, assess the applicability and limitations of reduced-order models in predicting turbine performance, and develop machine-learning surrogate models capable