20 machine-learning "https:" "https:" "https:" "https:" "UCL" "UCL" positions in United Kingdom
Sort by
Refine Your Search
-
Category
-
Program
-
Employer
-
Field
-
Partnership between UCL and AstraZeneca and to work as part of a cross-disciplinary team across both sites (London and Cambridge). This post is focused on the use of machine learning models of protein
-
. This role is eligible for hybrid working with a minimum of 60% of time on site. For a full job description please visit UCL’s online recruitment portal (https://www.ucl.ac.uk/work-at-ucl/search-ucl-jobs ) and
-
holiday (27 days annual leave 8 bank holiday and 6 closure days) Defined benefit career average revalued earnings pension scheme (CARE) Visit https://www.ucl.ac.uk/work-at-ucl/reward-and-benefits to find
-
classification algorithms including machine learning); and the output data and interpretability. The project “SORS in the community” is funded by the EPSRC (https://www.ukri.org/news/new-tools-aim-to-improve-early
-
. Interested candidates may want to take a look at our recent work on machine learning molecular dynamics: https://www.nature.com/articles/s41467-024-52491-3 Project 2: Non-adiabatic Molecular Dynamics
-
We are seeking a Research Associate with expertise in machine learning and causal inference to join the University of Manchester spoke of “CHAI hub: Causality in Healthcare and AI”. The CHAI hub
-
desirable with a willingness to learn new skills. The post holder will be required to work independently and as part of a team and be computer literate with excellent communication skills. This is an
-
. Recent advances in digital pathology and innovative data analytics including machine learning have enhanced our ability to identify clinically relevant spatial characteristics of TMEs [1]. In lung cancer
-
and cellular mechanisms shaping spatial and temporal trajectories of liver regeneration and cancer. Linking computer simulations with experimental observations will further uncover intrinsic and
-
transcript and protein levels. Using machine learning, we will identify conserved expression profiles that predict lifespan outcomes. Guided by these insights, we will use state-of-the-art genome editing in