604 computer-programmer-"https:" "https:" "UNIS" "https:" "https:" "https:" "https:" "I.E" Fellowship positions in United Kingdom
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major themes—cardiovascular science, neuroscience and immunology—addressing key organ systems targeted by non-communicable diseases. About the role The IDRM Transition Fellowship programme aims to support
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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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of presenting to the scientific community i.e. conference posters or talks. Applicants should submit CV with cover letter (max 2 pages) To be successful at shortlisting stage, please ensure you clearly evidence
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of presenting to the scientific community i.e. conference posters or talks. Applicants should submit CV with cover letter (max 2 pages) To be successful at shortlisting stage, please ensure you clearly evidence
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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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for the next generation. Fellowships will be for one year (i.e., 12 months), and are open to any postdoctoral, early career (tenure track) or tenured academic within 10 years of being awarded their PhD
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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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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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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