Sort by
Refine Your Search
-
Category
-
Employer
- University of Exeter
- University of Nottingham
- University of Cambridge;
- University of East Anglia
- Newcastle University
- University of Birmingham
- UNIVERSITY OF VIENNA
- University of Cambridge
- University of Surrey
- AALTO UNIVERSITY
- Abertay University
- Biology Centre CAS
- Durham University
- King's College London
- Manchester Metropolitan University
- Manchester Metropolitan University;
- Oxford Brookes University
- The University of Manchester
- University of Bath;
- University of Dundee;
- University of Newcastle
- University of Oxford
- University of Plymouth
- University of Sheffield;
- University of Warwick
- 15 more »
- « less
-
Field
-
therapies. In our laboratory, we have established and validated a panel of genetically heterogeneous PDAC models. These studies have shown that malignant-cell genetics shape distinct, therapeutically
-
that HbA1c can sometimes lead to incorrect management of diabetes. To address this problem, the project will use statistical genetics to untangle these distinct biological pathways. By separating the genetic
-
understanding of Lithium-ion battery systems, BMS, and battery models, including equivalent circuit and electrochemical models. Proven ability to develop and implement state estimation algorithms, such as Kalman
-
persist across participants, and retraining from scratch is often computationally infeasible at scale. In this project, you will develop the next generation of federated machine unlearning algorithms
-
candidate will design algorithms that identify suspicious model updates, reduce the influence of compromised UAVs, and preserve useful learning from honest UAVs operating with different data and unreliable
-
control platforms, advanced microcontrollers, distributed control algorithms, and artificial intelligence techniques, including neural networks and evolutionary optimisation methods, to enable the efficient
-
control platforms, advanced microcontrollers, distributed control algorithms, and artificial intelligence techniques, including neural networks and evolutionary optimisation methods, to enable the efficient
-
to evaluate and advance machine learning algorithms for one of the following application areas: “Characterising forests variations in relation to distance from pre-Columbian earthworks in the Amazon forest
-
an interdisciplinary team that works on cutting-edge questions ranging from mathematics and theoretical physics all the way to numerical simulation algorithms? Then apply now to join our team of researchers in
-
algorithms, validated in real-time simulations and experimentally in collaboration with industry. The project: This project looks at the development of electromechanical friction wheel braking (EMB) systems