Sort by
Refine Your Search
-
Listed
-
Employer
- ;
- University of Birmingham
- Imperial College London
- KINGS COLLEGE LONDON
- King's College London
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- UNIVERSITY OF SOUTHAMPTON
- Nature Careers
- University of Nottingham
- CRANFIELD UNIVERSITY
- Queen's University Belfast
- University of Oxford
- QUEENS UNIVERSITY BELFAST
- The University of Southampton
- University of Bristol
- University of Cambridge
- ; King's College London
- ; Technical University of Denmark
- ; University of Oxford
- Birmingham City University
- Cranfield University
- Manchester Metropolitan University
- UNIVERSITY OF MELBOURNE
- University of Glasgow
- University of Leeds
- University of Liverpool
- University of London
- University of Manchester
- University of Sheffield
- University of Stirling
- 20 more »
- « less
-
Field
-
the University's research culture and collaborative profile. Qualifications: PhD in Computer Science/AI or a closely related field. Extensive research experience in machine learning, deep learning, and self
-
communication journals Demonstrable proficiency in advanced quantitative data analysis: applied machine learning, statistical analysis, and handling complex data. Programming skills in Python and R are essential
-
are included but clinical medical themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data
-
developing ideas for application of research outcomes. This post also be linked to research activities linked to the Faculty’s research platforms such as the Power Electronics, Machines and Control Research
-
to assess potato dormancy break, including: data collection, processing, AI model development and classification accuracy assessment. Involved in supporting an electrophysiology-based machine learning model
-
indicators Experience in data visualisation and communication of research findings Track record of working effectively in international, multi-disciplinary teams Desirable Skills: Experience with machine
-
classification accuracy assessment. ii) Involved in supporting an electrophysiology-based machine learning model to predict dormancy break. You will be part of a multidisciplinary academic and industry team
-
for implementing the model as a computer simulation and analysing it within a health-economics framework using standard computational techniques. The post-holder will also be responsible for writing up the findings
-
processing, machine learning, neurophysiology, and human-centred design. You will be central to the execution and data collection of a major prospective research study in hospitalised patients. As a Clinical
-
development of future proposals for funding, into AI for renewable energy. You will consider ways in which the integration of machine learning algorithms might support the wider integration of, and uptake