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challenge. We seek a senior computational biologist to apply these extensive in-house datasets toward the development of novel, domain-tailored machine-learning models and analytical methods. You will explore
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of ethnographic or collaborative methods, as well as contributing to peer reviewed publications/presentations. You will also have excellent organisational skills and the ability to communicate effectively with
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GGR on UK lands. They will also have the opportunity to be part of a consortium project (ERW-GGR) which is itself a component of the UKRI funded Demonstrator phase of greenhouse gas removal techniques
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multiple aspects of work to meet deadlines. You will adapt existing and develop new scientific techniques and experimental methods, as well as contribute ideas for new research projects. You will use
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interpretability, explainability, and verification methods with an emphasis on white box methods and control techniques using causality. The post holder will work on projects that bridge technical AI capabilities
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for chronic fuel cell monitoring in vivo including common electrochemical characterization methods. • Design and manufacture an micromachined interface between the fuel cell and the implantable
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ultimately contributing to the development of new antiviral approaches. The project takes a cross-disciplinary approach, combining biochemical, biophysical, cell biological, and virological methods, including
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available data and apply causal inference methods, including Mendelian randomisation, to identify candidate mechanisms linking circadian misalignment and sleep disturbances with cardiometabolic disease
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. This can involve IoT connected devices, physical sensors or other instruments, including non-intrusive methods and inferences from a variety of data sources. You should have some experience with experimental
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consortium, the research involves elements of design, novel process and equipment development, modelling, manufacture, characterisation and data science. All applications must be made online using the Oxford