Sort by
Refine Your Search
-
Listed
-
Employer
- ;
- KINGS COLLEGE LONDON
- University of Oxford
- University of London
- DURHAM UNIVERSITY
- King's College London
- Durham University
- AALTO UNIVERSITY
- Heriot Watt University
- University of Cambridge
- Aston University
- Imperial College London
- Liverpool John Moores University
- Loughborough University
- University of Bristol
- University of Glasgow
- University of Liverpool
- University of Newcastle
- 8 more »
- « less
-
Field
-
cell culture and in vivo DDEB disease models, carrying equivalent disease causing single nucleoid variants and mimicking key features of the human disease. The Jacków-Malinowska lab has the opportunity
-
responsibility for implementing a deep learning work-package as part of a Cancer Research UK-funded programme, developing an image-recognition model to identify morphological features corresponding to clonal
-
responsibility for implementing a deep learning work-package as part of a Cancer Research UK-funded programme, developing an image-recognition model to identify morphological features corresponding to clonal
-
develop a comprehensive simulation framework that bridges microscopic semiconductor morphology with macroscopic charge transport dynamics, ultimately supporting the design of innovative touch sensor
-
experimental design (active learning) • Combining models and combining data / Realistic simulation of clinical trials • Developing LLMs to utilise ODEs and ProbML as tools; Code synthesis
-
candidate will be part of a research group with responsibility for carrying out research in Modelling, Simulation, and Analysis of Interacting particle systems and related fields as part of the Royal Society
-
criteria Experience in ex vivo and in vitro models of cardiovascular disease Experience in spatial transcriptomic, single cell sequencing or other -omics techniques and data analysis Experience in tissue
-
closely with data scientists to interpret and predict MFA data using nonlinear reaction-diffusion models, 13C-isotopomer analysis, and MATLAB-based simulations enhanced by Bayesian Machine Learning
-
be expected to: Develop transport simulation code for rapidly changing electrostatic environments Model carrier dynamics in disordered semiconductors Collaborate closely with experimental researchers
-
model physics, electroweak and Higgs physics, flavour physics, model building, Monte Carlo simulations, precision standard model physics, neutrino physics, particle astrophysics, strong interactions and