Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- University of East Anglia
- University of Nottingham
- The University of Manchester;
- Loughborough University
- The University of Edinburgh;
- The University of Manchester
- University of Cambridge;
- KINGS COLLEGE LONDON
- Newcastle University
- Newcastle University;
- UNIVERSITY OF VIENNA
- University of Birmingham;
- University of Bristol
- University of Cambridge
- University of Newcastle
- University of Surrey
- University of Warwick
- ;
- ; The University of Edinburgh
- ; University of Exeter
- Edinburgh Napier University;
- European Magnetism Association EMA
- Harper Adams University
- Imperial College London
- Imperial College London;
- King's College London
- King's College London Department of Engineering
- Loughborough University;
- Oxford Brookes University
- The Medicines And Healthcare Products Regulatory Agency;
- The University of Edinburgh
- University of Birmingham
- University of East Anglia;
- University of Exeter
- University of Exeter;
- University of Glasgow
- University of Leeds
- University of Oxford
- University of Oxford;
- University of Warwick;
- 31 more »
- « less
-
Field
-
) develop novel performance metrics combining accuracy and explainability, to be tested across different AI model types; (2) devise new algorithms for selecting models optimised for holistic performance
-
data are needed to enhance our understanding of sources, pathways and impact of litter. Cefas is developing a visible light (VL) deep learning (DL) algorithm and collected a large 89 litter category
-
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
-
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
-
-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
-
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
-
. Analysis of images will investigate the efficacy of manual digital approaches (e.g., Dot Dot Goose) and the development of a marine litter characterisation and quantification algorithm for automated analysis
-
samples. All computational methods and algorithms will be implemented as part of the python based MetaboLabPy platform (https://doi.org/10.3390/metabo15010048 , https://github.com/ludwigc/metabolabpy
-
conduct cutting-edge research on topics including, but not limited to: Complexity theory Quantum algorithms and complexity Sublinear algorithms Interactive proofs, PCPs, and zero-knowledge proofs
-
discovery, and deep-learning algorithms • Neutron scattering and advanced characterisation techniques The successful candidate will work closely with other PhDs and postdocs involved in similar investigations