348 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" positions at University of Oxford
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
responsible for supporting the delivery of various foresight research projects the Centre will be undertaking. This is an excellent opportunity to gain academic research experience and to learn from leading
-
thrusts within the lab’s multi-agent security programme. You should possess a completed PhD/DPhil (or thesis submitted by the start date) in Computer Science, Machine Learning, AI, Security, Robotics
-
for the project’s goal and a willingness to learn and develop new skills as the work evolves. Given the highly interdisciplinary nature of the role, you will be expected to collaborate closely with researchers from
-
sciences, AI, machine learning or related fields. Strong background and track record in the development of geospatial foundation models from multi-modal Earth Observations is essential as well as strong
-
An exciting opportunity has arisen for a Postdoctoral Research Assistant in the Department of Physics. Machine learning has made enormous progress during recent years, entering almost all spheres
-
students in the group. Candidates should have strong training in cross-disciplinary applied mathematics, with a demonstrated interest in biology, and experience in machine learning approaches is a plus. We
-
5 years. The appointments will be in the area of statistical quantitative finance/financial econometrics, in particular data science and machine learning applied to quantitative finance
-
funded by the University and by NaturalMotion Ltd. NaturalMotion Ltd develops computational methods for animating movement, as used in Hollywood films and computer games. The founder, Torsten Reil, is an
-
schemes Cycle and electric car loan schemes Employee Assistance Programme Membership to a variety of social and sports clubs Discounted bus travel and Season Ticket travel loans While this is a full-time
-
turbines represented using an actuator-line approach, assess the applicability and limitations of reduced-order models in predicting turbine performance, and develop machine-learning surrogate models capable