Sort by
Refine Your Search
-
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
-
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
-
-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
-
is conducted in collaboration with world-leading academic scientists from Imperial Chemistry and Urology, offering excellent opportunities to develop skills and networks. This post is funded by
-
. 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
-
an additional UK partner as well asfour Japanese institutions, with expertise in AI and/orlaw. To achieve the objectives, NeSyDebateswill develop and deploy novelforms of neuro-symbolic Computational
-
that this project will address are: (i) sensitivity to deformations and forces, (ii) repeatability and (iii) sampling rate, (iv) coverage area, (v) spatial resolution and (vi) temporal resolution. You will develop
-
to develop your skills and explore your career prospects. Sector-leading salary and remuneration package (including 43 days off a year and generous pension schemes). Be part of a diverse, inclusive and
-
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