62 computer-science-intern-"https:"-"https:"-"https:"-"https:"-"BioData" PhD positions at University of Nottingham
Sort by
Refine Your Search
-
, robotics, and machine learning. You will work within a multidisciplinary supervisory team spanning engineering, robotics, and computer science, and collaborate with researchers working on real-world
-
or computational frameworks, developed in collaboration with partners with modelling expertise. This PhD offers the opportunity to work at the interface of plant physiology, root biology, imaging and quantitative
-
centre for the development of medical imaging, particularly MRI. Eligibility You must be a university graduate or expecting to graduate with a 1st class degree in engineering, physics, computer science
-
we are looking for The candidate should have a 1st or high 2:1 degree in mechanical/aerospace/manufacturing engineering, computer science, physics, mathematics, or related scientific disciplines
-
science, 3D printing, biotechnology, and engineering. Eligibility and Application This call is open to UK-Home students and to International students. Project start date and duration: Start October 2026
-
will equip you with skills in materials science, 3D printing, biotechnology, and engineering. Eligibility and Application This call is open to UK-Home students and to International students. Project
-
researchers based in the School of Chemistry and in the Faculty of Engineering at the University of Nottingham and you will work closely with our industrial partners at Oil Spill Response Ltd (OSRL) . OSRL has
-
computational solid-state physics/ chemistry. Candidates with experience in the synthesis, the characterisation and performance testing or modelling of metal hydrides, complex hydrides and/or their composites
-
, Computer Science and the Biosciences. You will be supervised by Amanda Wright (Optics and Photonics Research Group, Faculty of Engineering), Mike Somekh (Optics and Photonics Research Group, Faculty
-
engineering excellence needed for the aerospace sector. In this PhD, high-fidelity two-phase Computational Fluid Dynamics (CFD) methods will be used to model complex and fundamental cryogenic hydrogen flows