564 cloud-computing-"https:" "https:" "https:" "https:" "https:" "St" "St" Fellowship research jobs in United Kingdom
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delivers a co-produced programme of research that serves two key purposes. It provides an evidence foundation to advance the institution's civic mission while creating meaningful impact where it's needed
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-institutional strategic national research programme dedicated to using data to transform our understanding of cancer risk and enable early interception of cancers. It represents a major, multi-million-pound
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programme, Deep Molecular Phenotyping of Metastatic Breast Cancer. This ambitious, interdisciplinary project aims to generate an integrated, multi-omic understanding of secondary (metastatic) breast cancer
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, computer vision, and predictive modelling, alongside excellent IT, analytical, interpersonal and communication skills in spoken and written English. You must be able to collect, process and present data with
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About the job: As a part of the 14CHRONO team, the research fellow will lead a programme of research and development into novel thermally-driven evolved gas analysis (EGA) molecular separation
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the Project PI. ii. Meeting the deliverables by carrying out the computational simulations and experimental studies planned for under the scope of work of the projects within the timeframe to meet the
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Studies. Working under the supervision of Professor Rita Abrahamsen (Chair of African Studies), the successful candidate will develop and pursue an ambitious and original programme of research within
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-intensive Universities? As a Research Fellow in Computational Climate Politics you will work on the UKRI FLF project “Understanding normative change to address the climate emergency ”. This is a 4+3 grant
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biggest challenges. We are seeking a Research Fellow in Liquid Metal Artificial Enzymes to join the School of Electronics and Computer Science (ECS) at the University of Southampton. The successful
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analytical backbone of the programme. It develops sensor-enabled diagnostic cells, multi-modal data pipelines and hybrid physics-informed machine learning approaches to understand interfacial behaviour during