Sort by
Refine Your Search
-
Listed
-
Employer
- ;
- University of Birmingham
- Imperial College London
- Nature Careers
- KINGS COLLEGE LONDON
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- UNIVERSITY OF SOUTHAMPTON
- University of Nottingham
- UNIVERSITY OF SURREY
- King's College London
- QUEENS UNIVERSITY BELFAST
- Queen's University Belfast
- The University of Southampton
- University of Cambridge
- University of London
- University of Surrey
- ; University of Oxford
- Birmingham City University
- CRANFIELD UNIVERSITY
- Queen's University Belfast;
- Technical University of Denmark
- UNIVERSITY OF MELBOURNE
- University of Glasgow
- University of Leeds
- University of Liverpool
- University of Manchester
- University of Oxford
- University of Sheffield
- 18 more »
- « less
-
Field
-
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
-
annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where
-
themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
-
themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
-
, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
-
, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
-
science and artificial intelligence concepts and tools to solve complex problems. Candidates will also be developing machine learning techniques and applying them at scale to specific projects with regular
-
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
-
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
-
the potential to impact on protected groups and take appropriate action. Desirable Skills: Experience with machine learning or natural language processing. Knowledge of econometric methods for policy evaluation