Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- ;
- Cranfield University
- ; The University of Manchester
- University of Cambridge
- University of Nottingham
- ; Swansea University
- ; University of Birmingham
- ; University of Nottingham
- ; University of Warwick
- Imperial College London
- ; Cranfield University
- ; Manchester Metropolitan University
- ; Newcastle University
- ; The University of Edinburgh
- ; University of Cambridge
- ; University of Leeds
- ; University of Oxford
- ; Aston University
- ; King's College London
- ; Loughborough University
- ; Technical University of Denmark
- ; UWE, Bristol
- ; University of Bradford
- ; University of Bristol
- ; University of East Anglia
- ; University of Essex
- ; University of Exeter
- ; University of Reading
- ; University of Sheffield
- ; University of Southampton
- ; University of Surrey
- ; University of York
- Abertay University
- Harper Adams University
- KINGS COLLEGE LONDON
- King's College London
- Newcastle University
- UNIVERSITY OF VIENNA
- University of Liverpool
- University of Newcastle
- University of Oxford
- 31 more »
- « less
-
Field
-
microbial communities. In this role, you will develop hybrid species distribution models that combine climate and landscape data to predict how microbial taxa niches shift under changing land use and
-
the availability and distribution of shaded pedestrian routes in Reading, with the aim of identifying priority areas for shade provision to support equitable and heat-resilient urban mobility. Green infrastructure
-
algorithms, validated navigation architectures, and new insights into next-generation intelligent mobility solutions. The student will undertake two industry placements at Spirent, use high-tech simulation
-
: Framework Development: Design and implement a generative deep learning framework for cross-modal integration and analysis, resilient to distribution shifts. Correlation Discovery: Identify interpretable
-
platform. Training the development digital-twin using real-time data from hardware available Electrical power level studies with developed digital twin to identify visible solutions for distribution electric
-
for distribution electric propulsion. Who we are looking for We are looking for enthusiastic, self-motivated applicants with first-class degree in Electrical Engineering, Aerospace Engineering or Computer Science
-
aims and objectives This project aims to develop an optimised, fault-tolerant implementation of the Falcon post-quantum digital signature algorithm for spaceborne FPGAs/processors. The key objectives
-
: The occurrence and distribution of species within and around solar parks, identifying key “winners and losers” in terms of biodiversity. How species interactions, including plant-pollinator networks
-
the form of a human-expert informed reward function. Second, we aim for the integration of low-energy machine learning algorithms, so that the resulting AI model can run on a variety of devices, including
-
workspaces under positional restrictions. Develop smart control algorithms that will allow the robotics end-effectors to communicate with the central control system and coordinate tasks with other end