41 phd-rehabilitation-engineering-computer-science Postdoctoral positions at University of Liverpool
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laboratory automation in a highly collaborative research environment. About you You will have a PhD (or equivalent) in a relevant area or field related to this project (Computer Science, Robotics, Mechatronics
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cutting-edge laboratory automation in a highly collaborative research environment. About you You will have a PhD (or equivalent) in a relevant area or field related to this project (Computer Science
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still awaiting your PhD to be awarded you will be appointed at Grade 6, spine point 30. Upon written confirmation that you have been awarded your PhD, your salary will be increased to Grade 7, spine
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Ecological Sciences at the University of Liverpool (UoL), UK. The position is funded by a Natural Environment Research Council (NERC) Pushing the Frontiers award 'CALDERA: Collaborative Approach to integrate
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facilities including the Materials Innovation Factory and the Centre for Long-Acting Therapeutics (CELT Global Health). You Should Have: - A PhD in chemistry, physics, engineering, materials science, or a
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of Computer Science and Informatics. This role offers a unique opportunity to work at the heart of a world-leading research hub, utilising our dedicated robotics chemistry laboratory and collaborating closely with
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novel plasma-disturbance sensing techniques. The role sits within the Department of Electrical Engineering and Electronics, part of the Faculty of Science and Engineering, which has a strong track record
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ecological models using the Oceananigans.jl infrastructure. This is an exciting opportunity to be part of an international collaboration (the Simons Foundation funded Computational Biology in Marine EcoSystems
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of the project we welcome applications from candidates who hold a PhD in a range of fields including Physics, Chemistry, Materials Science, Engineering. To apply for the position please follow the apply link and
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Computer Science, Chemistry, Chemical Engineering, Physics, or Materials Science. You will develop optimisation and machine-learning algorithms for human- and literature-informed discovery of new materials