387 computer-programmer-"https:"-"CNR"-"https:"-"https:"-"https:"-"https:"-"UNIV" Fellowship positions in United Kingdom
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Field
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develop foundational security theory for agentic and multi-agent AI systems, as part of our research programme in multi-agent security. You will build formal models of security-relevant behaviour in
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candidate will contribute to an interdisciplinary research programme focused on the design, synthesis, and characterisation of liquid metal nanomaterials as catalytic artificial enzymes for biomedical and
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wider research culture of the University. A key component of the role is the opportunity to develop your own programme of research. Applicants are therefore required to submit a research proposal (1,500
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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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undertaking a specified range of activities within an established research programme and/or specific research project. Role Summary Work within specified research grants and projects and contribute to writing
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candidate will contribute to an interdisciplinary research programme focused on the design, synthesis, and characterisation of liquid metal nanomaterials as catalytic artificial enzymes for biomedical and
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and proud members of the Stonewall Diversity Champions Programme. We are committed to actively exploring flexible working options for each role and have been ranked in the Top 30 family friendly
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candidate will contribute to an interdisciplinary research programme focused on the design, synthesis, and characterisation of liquid metal nanomaterials as catalytic artificial enzymes for biomedical and
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leadership. Responsibilities include coordinating major research programmes and partnerships, supporting training initiatives such as the Oxford EPSRC Centre for Doctoral Training in Health Data Science
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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