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platforms will be designed, capable of efficiently exploring complex mixed variable design spaces on the microlitre scale. Our multisite reactor network will be driven by next generation machine learning
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be part of a small team, led by Dr Susanne Bornelöv, which studies gene regulation using various approaches including machine learning, evolutionary genomics, and sequencing bioinformatics. Our team is
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candidate will have a PhD in a strongly quantitative discipline, but applications from candidates close to submitting their PhD are also welcome. Familiarity with deep learning methodologies is essential, as
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protein design and evolution, using molecular biology and biophysics along with the latest AI or machine learning tools. According to the development of the project there may be the chance to learn other
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grant funding remaining in place. Holding a PhD in Music or a related discipline and with a record of, or clear potential for, outstanding research, you will be a scholar whose research activities fall
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. Candidates must have a PhD in a relevant field and sufficient breadth and/or depth of specialist knowledge in their chosen discipline to be able to teach to a high standard. They should also have evidence of
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. The PDRA should have a relevant specialist area in data science, including managing and structuring data, programming, developing and applying theoretical methods and machine learning models for data
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successful innovation cluster, you will be working with cutting edge technologies and will be able to help these discoveries make a difference in the world. What we're looking for: You will likely have a PhD
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mechanisms that support learning and brain plasticity. Development Opportunities: Our collaborative research programme provides unique opportunities for cross-disciplinary training in innovative methodologies
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an interest to apply their skills in the context of a mechanobiology project. There will be ample opportunity to learn from and become immersed in a research group that utilizes a range of biological systems