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3 years. The project builds on the expertise of the Fuchter group in the design, synthesis and study of photoswitchable compounds and their application to functional systems, with a particular
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PPIE activities is required. This is an exciting opportunity for someone to make a real impact on understanding how Parkinson’s progresses and to shape future research on prediction and treatment. About
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disciplines and contributing to a collaborative team effort. You are proactive, organized, and motivated to help build new experimental systems while also extracting biological insight from complex datasets
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research project led by Dr Gemma Brierley, whose group investigates the molecular underpinnings of insulin resistance. This project builds on our previous work using agonistic antibodies to activate mutated
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Postdoctoral Positions for Computational Genomics, Cancer Genetics, and Translational Cancer Biology
to make high-impact discoveries with direct clinical translational potential. Why Join Us? “Dark matter” cancer genetics: Pioneer discoveries in uncharted areas of breast cancer genetics, including
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arranged so that they allow for the best possible work-life balance; there is a strong focus on vitality and you can make use of the sports facilities available on campus for a small fee; a fixed year-end
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Materials science and technology are our passion. With our cutting-edge research, Empa's around 1,100 employees make essential contributions to the well-being of society for a future worth living
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of switchable RNA nanostructures. Develop databases for RNA modules for automated building of atomistic models. Develop multistate sequence design algorithm for rational design of RNA switches. Develop database
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includes the following tasks: Develop computer-aided design software for modular construction of switchable RNA nanostructures. Develop databases for RNA modules for automated building of atomistic models
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We have an exciting opportunity for a Research Assistant/Associate to make a contribution to an ERC-funded project "Dynamic network reconstruction of human perceptual and reward learning via