278 computer-"https:"-"APOS-UFFICIO-CONCORSI-DOCENTI" "https:" "https:" "https:" "https:" "https:" positions at University of Sheffield in United Kingdom
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previous experience of using CFD would be useful but not essential. Funding Notes The programme is four years and starts in September 2026. Funding includes full UK fees, tax-free stipend (2025/2026 stipend
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are on social media analysis, multilinguality and multimodality, machine translation, and AI for NLP. https://sheffield.ac.uk/cs/people/academic/carolina-scarton About the School The School of Computer
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programming would be advantageous but are not essential. How to Apply Notes and details of how to apply are available here: https://accedtp.ac.uk/how-to-apply/ All applicants to ACCE+ must complete an online
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, aerodynamic noise, machine learning, stochastic algorithms, uncertainties This University of Sheffield PhD project is part of the EPSRC Centre for Doctoral Training in Sustainable Sound Futures programme
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BBSRC Yorkshire Bioscience DLA Programme: Harnessing JAK-STAT signalling for optimal innate immune cell function School of Medicine and Population Health PhD Research Project Competition Funded
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BBSRC Yorkshire Bioscience DLA Programme: Nuclear Myosin VI supports DNA repair and is a potential therapeutic target School of Medicine and Population Health PhD Research Project Competition
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BBSRC Yorkshire Bioscience DLA Programme: Deciphering the mechanism of apicoplast genome organisation in Plasmodium falciparum (CASE project) School of Biosciences PhD Research Project Competition
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potential diversion towards military applications. For example, the UK’s Spherical Tokamak for Energy Production (STEP) programme aims to deliver a prototype fusion power plant by 2040, which is expected
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pathways interact to control germination. This project will provide training in bacterial physiology, infection biology, quantitative microscopy and computational data analysis — a diverse skill set that
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skill via an intrinsic reward. Science Advances, 10(13), eadj3824. https://www.science.org/doi/full/10.1126/sciadv.adj3824 Durstewitz, D., Koppe*, G., & Toutounji*, H. (2016). Computational models as