Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- Newcastle University
- University of Exeter
- University of Exeter;
- University of Sheffield
- Imperial College London;
- The University of Manchester
- University of Birmingham
- University of Cambridge;
- University of Plymouth;
- ;
- AALTO UNIVERSITY
- Imperial College London
- KINGS COLLEGE LONDON
- King's College London
- King's College London Department of Engineering
- Swansea University;
- University of Birmingham;
- University of East Anglia
- University of East Anglia;
- University of Glasgow
- University of Nottingham
- University of Strathclyde;
- 13 more »
- « less
-
Field
-
motivated PhD candidate with interests and skills in computational modelling and simulations, fluid dynamics, mechanical engineering, physics and applied mathematics. You should have experience in one or more
-
). Additional project costs will also be provided. Overview We are seeking a highly motivated PhD candidate with interests and skills in computational modelling and simulations, fluid dynamics, mechanical
-
and night-time low temperatures. There is a need to improve the way the stratified boundary layer is represented (parametrized) in these simulations and also interrogate the models with high-quality
-
integrates real-time environmental data and physiological information to simulate and forecast how marine turtle populations respond to changing thermal conditions. The proposed Digital Twin for Marine Turtle
-
modelling framework to predict key thermal hydraulic parameters for boiling flows within complex geometries at high heat flux conditions, relevant to the engineering design of thermal management elements
-
seasonal-to-subseasonal forecasting ensemble in modelling and forecasting these processes. As datasets develop, there may also be opportunities to assess simulation skill of AI forecasts. For further
-
, GNSS positioning is highly susceptible to errors from atmospheric distortions, multipath effects, and receiver noise. Recent advances in deep learning have shown that data-driven pseudorange correction
-
and model inaccuracy. This role is dedicated to solving this problem, building an embedded plume-in-grid contrail model for use in an operational weather model - supporting technological solutions
-
technology increases the grid’s exposure to cyber-attacks, which can compromise measurement signals, disrupt control commands, or induce model or data-driven instability. This project aims to develop a robust multi
-
; Nguyen et al., 2023). By integrating large scale, multi-modal data and leveraging self-supervised and transfer learning, these models demonstrate satisfactory spatial-temporal simulation and predictions