Sort by
Refine Your Search
-
Listed
-
Employer
- University of Oxford
- ;
- KINGS COLLEGE LONDON
- University of London
- AALTO UNIVERSITY
- Heriot Watt University
- UNIVERSITY OF VIENNA
- University of Cambridge
- Durham University
- Imperial College London
- King's College London
- University of Glasgow
- ; Queen Mary University of London
- ; University of Oxford
- City University London
- Nature Careers
- Nottingham Trent University
- University of Liverpool
- University of Manchester
- University of Nottingham
- University of Reading
- University of West London
- 12 more »
- « less
-
Field
-
Travel ” which examines signal processing and machine learning methods for inferring active travel activities from optical fibre signals. About You Applicants must have an Undergraduate Degree in
-
skills and experience: Essential criteria PhD qualified in relevant subject area* Experience developing deep learning segmentation models Experience with Pytorch, MONAI, CUDA or equivalent software
-
scientific publications, patents, and seeing collaborators translate our work into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and
-
methods suitable for legged systems in physically-realistic simulated environments and on real robots. You should hold or be close to completion of a PhD/DPhil in robotics, computer science, machine
-
), to develop systems that improve the efficacy of machine learning-based technologies for healthcare applications. You must hold a PhD (or be near completion) in a field such as AI, computer science, signal
-
learning. The post-holder will be familiar with the use of these techniques and experience of dataset construction and data mining will be essential. The successful applicant will have completed an MPhil/PhD
-
superconducting qubits and millikelvin electronics Did you recently get your PhD in circuit quantum electrodynamics (cQED) and are now looking into taking the full potential of your skills into use for making new
-
machine learning, computer vision, human-computer interaction, or similar relevant areas. Experience in research or development on bias, interpretability, and/or privacy in machine learning/AI is necessary
-
vision research. The department fosters interdisciplinary collaboration, addressing real-world challenges through innovative machine learning, data science, and intelligent systems research. About the role
-
computational analyses of epigenomic/transcriptomic data and machine learning. Experience in single-cell omics data is desirable. The post holder will be responsible to develop pipelines for the analysis