-
of Ophthalmology and will further develop, expand, and diversify Oxford’s portfolio of internationally competitive research in Ophthalmology/Visual Science. The Chair will develop and lead their own programme of
-
for network constraints and uncertainty. Collaborating with other researchers on the project, you will also contribute to the design of a supporting cloud-to-edge computing architecture and profit-sharing
-
the possibility of extension subject to funding. The UK Programme of Laser Inertial Fusion Technology for Energy (UPLiFT) is a 4 year £10M research programme funded by the UK government’s Department for Energy
-
computational modelling using artificial neural networks. It brings together teams led by Mohamady El-Gaby (Oxford Experimental Psychology), Matthew Nour (Oxford Psychiatry), Rick Adams (UCL), and Maria Eckstein
-
Alliance. Other duties will include contributing to community activities such as seminars and networking events and developing skills in many areas of computational biological research via independent study
-
robust deep neural networks applied to computer vision tasks. You should have the ability to manage your own academic research and associated activities. Informal enquiries may be addressed to Professor
-
of extension depending on funding. The Oxford Ion Trap Quantum Computing group currently hosts one of the world’s highest performance networked quantum computing demonstrators, capable of remote Bell-pair
-
modelling of age-relevant mutations in the human osteocytic network function. You will be based at the Botnar Research Centre within the Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal
-
Science, Engineering or a related scientific discipline. You will have knowledge and experience in wired computer networks, with a focus on low latency interconnect or related areas. Prior experience of python and C/C
-
to develop superconducting microwave interconnects and metasurfaces for distributed quantum networks, focusing on high-fidelity none-nearest neighbour entangling gates while addressing hardware overhead and