69 condition-monitoring-machine-learning Postdoctoral positions at University of Oxford
Sort by
Refine Your Search
-
research will focus on leveraging this interplay to monitor thermodynamic quantities such as work and heat, aiming for direct measurements of work exchange in non-equilibrium quantum systems. The appointed
-
projects. It is essential that you hold a PhD/DPhil in a quantitative or computer science related subject (e.g. Statistics, Machine Learning, Biostatistics, AI, Engineering), and have post-qualification
-
-responsive molecular machines. The project is funded by the European Research Council. Find out more about the Langton research group at: here About you Applicants must hold a PhD in Chemistry or a related
-
team working with epidemiologists, parasitologists, mathematicians, machine learning scientists, laboratory technicians, field assistants, health practitioners/policymakers, and global health ethicists
-
transparent windows to separate a liquid/gas environment from the vacuum conditions typically needed (see 10.26434/chemrxiv-2025-0cgp0). This role therefore aims to translate this approach from the synchrotron
-
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
-
and molecular mechanisms that act within these sites, and to determine how these events lead to a balanced immune response in different conditions and physical constrains imposed by each organ
-
immunogenicity experiments, murine aerosol challenge experiments, administration of vaccines and drugs, monitoring of in vivo, collection and processing of samples, etc. You will be involved in setting up assays
-
projects the Centre will be undertaking. This is an excellent opportunity to gain academic research experience and to learn from leading academics. The ideal candidate for this role will have a background in
-
interaction, safe exploration, long-term monitoring). You will be responsible for developing new techniques for single- or multi-agent decision-making under uncertainty in the context of autonomous systems