-
language processing (large language models) to investigate the brain computations supporting planning in humans, and how this can go awry in psychosis. What We Offer As an employer, we genuinely care about our
-
academics working on biogeochemistry in the Department. About you You will hold, or be close to completion of, a relevant PhD/DPhil, together with relevant experience. You will possess sufficient specialist
-
Computational Methods for Advanced Research to Transform Biomedicine ( SMARTbiomed ), an international collaboration that integrates large-scale, multimodal biomedical data with advances in statistical and
-
forms part of a large multi-institution collaborative project entitled Next Generation Electrodes (Nextrode) funded by the UK’s Faraday Institution. The appointed person will collaborate with other
-
candidate will be a member of a large multi-disciplinary team working on UPLiFT’s Physics work package (others include the development of IFE lasers and implosion targets). At plasma conditions such as those
-
synaptic function and synaptic loss, and keep meticulous, detailed records of your work and commit to engaging with cloud-based analyses on the IMCM data platform. Other duties will include collaborating
-
targets, by leveraging high-dimensional big data (e.g. electronic health records, multi-omics and phenotypic) from large prospective biobanks including China Kadoorie Biobank (CKB) and UK Biobank (UKB), and
-
of progression to secondary acute myelogenous leukaemia (sAML). You will take a lead on developing data analysis approaches to search for targetable genetic, epigenetic, or epitranscriptomic mechanisms
-
applicant must hold a PhD/DPhil in a relevant subject. They must have peer-reviewed publications using data science approaches, for example, genetic analysis, including Mendelian randomisation and genetic co
-
together with relevant experience; have expertise and experience in survey design, quantitative research methods, and advanced statistical analysis; experience in managing large-scale data collection