Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- University of East Anglia
- University of Nottingham
- ; The University of Manchester
- The University of Manchester
- ;
- University of Sheffield
- ; Newcastle University
- ; Swansea University
- ; University of Southampton
- University of Birmingham;
- ; Brunel University London
- ; Loughborough University
- ; St George's, University of London
- ; The University of Edinburgh
- ; University of Cambridge
- ; University of East London
- ; University of Exeter
- ; University of Warwick
- Brunel University London
- Coventry University Group;
- Harper Adams University
- Imperial College London
- King's College London;
- Loughborough University;
- UNIVERSITY OF EAST LONDON
- University of Birmingham
- University of Cambridge
- University of Exeter;
- University of Glasgow
- University of Nottingham;
- University of Sheffield;
- University of Strathclyde;
- University of Warwick;
- 24 more »
- « less
-
Field
-
and individual fitness, including determining the added value (beyond metrics of inbreeding) of such scores in predicting fitness 2) Quantify drift load (the reduction in fitness caused by deleterious
-
://doi.org/10.1039/D2CC00532H ) that have potential applications in sensing, separations and catalysis. Our research focusses on three distinct challenges to achieving efficient material prediction: i
-
of Health and Life Sciences. Prostate cancer is highly heritable and a good target for genetic risk stratification. Prostate cancer genetic risk scores (GRS) aggregate common variants into a predictive score
-
. Your work will feed directly into the development of predictive models that link microstructure to performance, guiding the design of alloys that are stronger, more reliable, and more efficient. By doing
-
into quantitative frameworks for prediction of the contribution of An. stephensi to malaria transmission, and optimising surveillance and control for this and other native vector species in urban settings. 2. Build
-
signs of cardiovascular changes, adaptively model physiological patterns, and identify predictive biomarkers of maternal health. You will develop and apply cutting-edge techniques in: Signal processing
-
that can be validated with experiments and bottom-up models at multiple scales in order to predict the macroscopic response. Hence, this research will investigate the degradation of metallic materials under
-
of the complex physics governing the interaction between the heat source and the material. Additionally, it seeks to develop an efficient modelling approach to accurately predict and control the temperature field
-
-aware predictions. The successful candidate will join an international, interdisciplinary team and contribute to AI solutions with direct impact on biodiversity monitoring, conservation planning, and
-
bioinformatic skills to predict the evolution of rare diseases? FSHD is a rare neuromuscular disorder. No approved treatment is currently available. Slow and variable disease progression complicate trial design