179 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at University of Oxford
Sort by
Refine Your Search
-
Listed
-
Field
-
projects. It is essential that you hold a PhD/DPhil in a quantitative or computer science related subject (e.g. Statistics, Machine Learning, Biostatistics, AI, Engineering), and have post-qualification
-
the UKRI through the Frontier Guarantee Programme to Dr Jani R Bolla. The work is to be conducted in his lab in the Department Biology, University of Oxford, South Parks Road, Oxford, OX1 3RB
-
. The post-holder will have the opportunity to teach. Applicants should hold PhD in Astrophysics or a related field. A strong background in Radio interferometry observation and sufficient specialist knowledge
-
and Prof Paul Shearing. The post is funded through a strategic research partnership and is fixed term for up to 2 years. To support the programme, the post holder will be required to carry out research
-
-responsive molecular machines. The project is funded by the European Research Council. Find out more about the Langton research group at: here About you Applicants must hold a PhD in Chemistry or a related
-
We are seeking a full-time Postdoctoral Research Assistant to join a cross disciplinary research project to improve our understanding of colorectal cancer. Deep learning has revolutionised image
-
and Mind Building, South Parks Road, Oxford Applicants must hold a PhD in Microbiology and/or Molecular biology and will be responsible for providing microbiological data to facilitate the design of new
-
with an international reputation for excellence. The Department has a substantial research programme, with major funding from Medical Research Council (MRC), Wellcome Trust and National Institute
-
the Department of Engineering Science at the University of Oxford. The post is funded by the Oxford Martin Programme on Circular Battery Economies. It is fixed term up to December 2027. You will undertake
-
research initiative funded by ARIA, titled Aggregating Safety Preferences for AI Systems: A Social Choice Approach. The project operates at the interface of AI safety and computational social choice, and