Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- University of Nottingham
- The University of Manchester
- University of Cambridge;
- University of East Anglia
- Imperial College London;
- Loughborough University
- Newcastle University;
- University of Bristol
- University of Cambridge
- University of Oxford;
- University of Surrey
- University of Warwick
- ;
- ; University of Exeter
- Cranfield University;
- European Magnetism Association EMA
- Newcastle University
- Oxford Brookes University
- Swansea University;
- The Medicines And Healthcare Products Regulatory Agency;
- The University of Manchester;
- UCL;
- UNIVERSITY OF VIENNA
- University of Birmingham
- University of Birmingham;
- University of Exeter
- University of Exeter;
- University of Glasgow
- University of Kent;
- University of Leeds
- University of Newcastle
- 22 more »
- « less
-
Field
-
models optimised with evolutionary algorithms to address combinatorial optimisation in model design and the noisy nature of climate data. The Doctoral Researcher will receive on-the-job training in machine
-
. Exposure to neural-symbolic algorithms for transforming intent into conformant security or safety policy and/or enforcing security controls is optional but beneficial. Research will also give the opportunity
-
implementing AI algorithms to deliver safer and more efficient care. The student will have access to a unique training programme in AI in healthcare and health data science as well as a wide range opportunities
-
representation. Key aims include improving the generalizability, interpretability, reasoning and causal grounding of these models, developing new optimisation algorithms with biologically meaningful regularisation
-
Zero transport strategy. Outcomes will include novel AI algorithms, validated navigation architectures, and new insights into next-generation intelligent mobility solutions. The student will undertake
-
creating robust, low cost, and real-time edge-AI algorithms capable of accurately classifying diverse marine species and debris under complex and dynamic underwater conditions. The demand for such a low-cost
-
qualifications will be considered. Experience of using machine learning algorithms and toolsets, ideally in a research context. Strong programming skills (e.g., Python, Java, C++) An interest in physiological
-
-terminal antennas and beamforming operating in FR1 bands and future FR-2, enabling robust terrestrial–satellite integration for safety-critical air mobility services. To develop AI-based algorithms
-
-Making and Route Optimisation: Develop adaptive algorithms within a bias-aware ensemble Kalman filter framework to propose alternative flight paths dynamically. The system will aim to maximise safety and
-
algorithms based on neural activity data (local field potentials, LFPs) from key deep brain stimulation targets including the basal ganglia and thalamus. Auxiliary data available to implanted devices include