16 developer-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" research jobs at Imperial College London
Sort by
Refine Your Search
-
Do you want to combine high-throughput directed evolution with machine-learning analysis of deep sequencing data to engineer better antibodies? The Sormanni Lab in the Department of Chemical
-
This post (Research Assistant/Associate in Computational Methods for Structural Wing Design) will allow you to contribute to a major industry-funded project developing the next generation of
-
phenotypes. You will develop and evaluate models of cardiovascular outcome risk, and health economic models that put a cost, a quality-adjusted life year and an inequality consequence against each alternative
-
of materials with synthetic motion. The successful applicant will be expected to work with Professor Riccardo Sapienza in London and our experimental collaborators to develop experiments in the field
-
College London. The goal of SPARK is to train a top-class cohort of 15 DCs, that will become the future R&D staff within the area of spatiotemporal metamaterials and light fields. We will create and
-
the development of novel therapeutics for the treatment of prostate cancer. You would be joining an expanding group, undertaking collaborative and multidisciplinary work with the aim of progressing a novel DNA
-
-on protein science with automation, and carries genuine responsibility for the instruments, procedures and sustainability standards of a laboratory that is still taking shape. You will develop and deploy
-
collaborative environment where researchers, students and partners work together to develop transformative new ideas, train future leaders in AI, and strengthen the UK's position at the forefront of global AI
-
Foundation Model inference across the cloud continuum, spanning two main tasks. Firstly, the post-holder will develop simulation-based and analytical methods to evaluate adaptive caching policies
-
. The programme uses large-scale real-world datasets to investigate metabolic dysfunction in type 1 diabetes and its relationship with clinical outcomes, and to develop and validate prediction models that support