Sort by
Refine Your Search
-
Listed
-
Employer
- University of Oxford
- ;
- Durham University
- University of Cambridge
- DURHAM UNIVERSITY
- AALTO UNIVERSITY
- Heriot Watt University
- KINGS COLLEGE LONDON
- UNIVERSITY OF VIENNA
- ; University of Oxford
- City University London
- Imperial College London
- King's College London
- University of Liverpool
- University of London
- 5 more »
- « less
-
Field
-
machine learning methods to improve the understanding, treatment and prevention of human disease. The successful candidate will develop novel statistical and machine learning algorithms to address key
-
extracellular vesicles with **GENE**-mRNA; • In vivo delivery of first vector and evaluation of expression/distribution; • In vivo delivery of first vector in acute MI and evaluation
-
**-mRNA; In vivo delivery of first vector and evaluation of expression/distribution; In vivo delivery of first vector in acute MI and evaluation of expression/distribution; Analysis of response at the level
-
encouraged to adopt a creative approach to problem-solving, exploring various deep learning techniques. Verification of these models and algorithms will be conducted using benchmark datasets and real-world
-
to reconstruct the tree-of-life on Earth, it allows us to reveal how biological function has evolved and is distributed on this tree, and it is the foundation that enables us to use model organisms
-
dynamics, solid mechanics, soft matter or active matter. • To become familiar with simulation algorithms as needed, assist in the development of new ones, test and document any newly developed
-
developing new algorithmic approaches for TAPS data, interpreting the results in the context of phenotypic observations, and communicating these findings clearly to the broader team. You will prepare the
-
developed goal-sequence generalization task. The project will integrate high-density silicon probe recordings, optogenetics, pharmacology and advanced computational tools to analyse neural algorithms
-
precisely monitor changes in the global distribution of CO₂ sources and sinks. The UK science lead for MicroCarb is at the University of Edinburgh so this opportunity is a valuable opportunity to gain a deep
-
type (iv) work with the computational biology team to transfer this information into a AI algorithm that can distinguish neurodegenerative and neuroprotective phenotypes (v) work with colleagues in