51 web-developer-"https:"-"https:"-"https:" PhD positions at University of Nottingham
Sort by
Refine Your Search
-
Turbulence detection in blood flow using 4D MRI Flow disturbances in blood flow are vital sign of cardiovascular diseases, suggesting a development of turbulent flow due to abnormal heart movement
-
collaboration with BP. Vision We are seeking PhD student that is motivated to develop practical rapid analysis tools for BPs biomass liquefaction processes. We are looking for candidates with an interest in
-
relying on incremental optimisation of existing materials. By developing novel multilayer dielectric materials with ultra-high breakdown strength, the research will revolutionise electrified technologies
-
one of the world’s leading centres for additive manufacturing research and development, invites applications for a fully funded PhD programme. Metal additive manufacturing is transforming how complex
-
). The AI DTC is an initiative by the University of Nottingham to develop future researchers and leaders who can address key challenges of the 21st century through foundational and applied AI research
-
at the tumour margin represent a key target for earlier and more effective therapeutic intervention. This PhD project will develop advanced MRI analysis methods and imaging-driven predictive models focusing
-
placement within Siemens Digital Industry Software. Project Overview The project focuses on developing and integrating ML techniques to enhance wall treatments for under-resolved boundary layers in
-
simulation, composites manufacturing and advanced sensing techniques. The project will provide opportunities to develop skills in these areas and contribute to the development of the next generation composite
-
Airbus’ ZEROe concepts. However, liquid hydrogen fuel systems remain largely unstudied and critical fundamental research and modelling capability needs to be developed to strengthen the necessary
-
the noise associated with near-term quantum devices. This in turn offers an exciting new dataset from which it will be possible to use machine learning to train a more accurate functional for use in density