Sort by
Refine Your Search
-
the lunar farside. The successful candidate will contribute to the development and scientific programme of the Lunar Farside Technosignature and Transients Telescope (LFT3; https://lft3.space). Lunar Farside
-
for carrying out research on the European Research Council Project METACOMP on “Meta-Complexity: A Unified Approach to the Complexity of Proofs and Computation”, with Rahul Santhanam as Principal Investigator
-
collaborative programme bringing together a team of leading experts in advanced electron microscopy imaging, first-principles modelling, metal halide semiconductor thin-film and device fabrication, and
-
opportunity to teach. Applicants should possess or be close to obtaining a PhD in physics, materials science, or physical chemistry. They should be highly experienced in advanced first principles computational
-
criteria: • Relevant PhD/DPhil (or be near completion), in engineering, physics, mathematics, or similar, together with relevant experience • Strong background in mathematical optimisation
-
and testing of LLMs. About you You will hold, or be close to completion of a PhD/DPhil in a relevant technical subject (e.g. computer science, statistics, engineering) and possess sufficient specialist
-
, and Michigan, and also link to other worldwide members of the tskit community. You will hold or be close to completion of a relevant PhD/DPhil in a quantitative subject (e.g. statistics, mathematics
-
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
-
, participating actively in group discussions, and potentially contributing to undergraduate or graduate teaching. Applicants should hold (or be close to completing) a PhD in computational/theoretical chemistry
-
of flexible or part‑time working. The Project Working under the supervision of Professor Nicole Grobert, you will contribute to a collaborative research programme focused on applying AI‑driven analysis to in