114 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "UCL" research jobs at University of Oxford
Sort by
Refine Your Search
-
Listed
-
Country
-
Program
-
Field
-
to the 30th September 2026. We are looking for outstanding machine learning researcher to join the Torr Vision Group and work on AI Scientists: systems that use foundation models, AI agents, and robotics
-
* together with relevant experience. You will have a strong technical background in machine learning, especially RL and LLMs. An ability to work independently and as part of a collaborative research team is
-
the leadership of Principal Investigator Dr Andrew Siemion. Listen's interdisciplinary research has synergies with many of the department's research priorities, including exoplanet studies, machine learning
-
machine learning methods to model changes in the brain over the lifespan, including brain structure and function, and how those changes relate to environment and genomics. What We Offer As an employer, we
-
on “Active exploration of iridescence and gloss”. The ESR will join the EXPLORA consortium (https://explora-network.github.io/web/index.html ), which comprises 12 academic institutions across multiple European
-
the sequence of the human genome and the development of common diseases. You will work on a collaborative project that aims to develop Machine Learning and laboratory-based approaches, for decoding how the human
-
demonstrating an understanding of the needs and expectations of young people in an informal learning environment. You will be an organised and confident communicator, able to work independently while also
-
work and personal life - https://hr.admin.ox.ac.uk/staff-benefits Committed to equality and valuing diversity We welcome applications from individuals from all backgrounds, including those under
-
statement, CV and the details of two referees as part of your online application. Please see the University pages on the application process at https://www.jobs.ox.ac.uk/application-process The closing date
-
, socially grounded approaches to disease threats affecting animal and human health — learning from those who manage biological risks in everyday settings rather than relying solely on top-down models. Working