Sort by
Refine Your Search
-
supporting statement, setting out how you meet the selection criteria for the post, using examples of your skills and experience. As part of your application you will be asked to provide details of two
-
molecular evolution, particularly in viruses with epidemic and pandemic potential, as well as those with significant implications for plant and animal health. This post is part of a Wellcome Trust-funded
-
phenotyping. We are seeking for an enthusiastic Post Doctoral Research Assistant (PDRA) with strong experience in bioinformatic analyses to join our team and lead the analyses of bulk and single cell ‘omic
-
committed to equality and valuing diversity. All applicants will be judged on merit, according to the selection criteria. This post is full time with a start date to be agreed with the successful candidate
-
publications/presentations and the ability to manage your own academic research and associated activities are essential. The post is available fixed-term until 31 December, 2027, funded by Cancer Research UK
-
will be expected to manage your own academic research and administrative activities. This involves small scale project management, to co-ordinate multiple aspects of work to meet deadlines. The post will
-
) at the University of Oxford, Old Road Campus in Headington, Oxford. The unit undertakes research about pregnancy, childbirth and newborn babies. This is an exciting opportunity for a post-doctoral Qualitative
-
well as strong computing skills, including the knowledge of UNIX/Linux, Fortran, Python, or other high-level languages. The post is full time and fixed term for 3 years. The closing date for applications is noon
-
academic research and associated activities are essential. The post is available fixed-term until 31 December, 2027, funded by Ludwig Cancer Research. Applications for this vacancy are to be made online. You
-
). The post is funded by NIHR and is fixed-term for 24 months, with a possible extension. This project is about creating novel AI models to predict patient outcomes following acceptance or refusal of an offer