160 high-performance-quantum-computing-"https:" "https:" "https:" positions at Imperial College London
Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
, United Kingdom of Great Britain and Northern Ireland [map ] Subject Areas: Physics / Gravitational Theory , Gravity , hep-th , high energy physics or mathematical physics , Particle/Cosmology Theory
-
Join Us in Investigating Climate Health Impacts from Heat Worldwide! Are you a forward-thinking, innovative individual fascinated by the challenges of climate-related health impacts across the globe
-
Salam Centre for Theoretical Physics at Imperial College London. We undertake world-leading research in key areas of theoretical physics including string theory, quantum field theory, cosmology, quantum
-
imaging, physiological measurement, machine learning, and high-performance computational modelling. You will work with cardiology, cardiac electrophysiology, imaging, and biomedical engineering teams
-
efficient processes and governance frameworks to support programme growth. You will manage and develop a high-performing team, building capacity to deliver complex deep-science accelerator sessions and multi
-
areas of cosmology, gravity, particle physics, and quantum theory and comprises Profs. Shai Chester, Carlo Contaldi, Claudia de Rham, Fay Dowker, Tim Evans, Jerome Gauntlett, Jonathan Halliwell, Amihay
-
Multi-Scale Modelling Project, working within a nationally coordinated programme tackling the most pressing challenges in battery science. Playing a key role in advancing PyBaMM (https://pybamm.org
-
help create the next generation of doctors and scientists—benefiting patients and populations globally. We are seeking a highly motivated individual to join our team as Programme Administrator
-
European partners in a transnational network, implementing a multidisciplinary and intersectorial research and training programme between the academic and industrial partners, to research the self
-
these extreme events across a series of complex flows. This will entail performing high-fidelity simulations of a range of flows exhibiting extreme events, developing hybrid physics-based/machine learning