Sort by
Refine Your Search
-
Listed
-
Employer
- University of Oxford
- KINGS COLLEGE LONDON
- ;
- DURHAM UNIVERSITY
- Durham University
- University of Cambridge
- University of Oxford;
- AALTO UNIVERSITY
- King's College London
- Heriot Watt University
- UNIVERSITY OF VIENNA
- Nature Careers
- The University of Edinburgh;
- University of Liverpool
- ; University of Copenhagen
- Aston University
- Heriot-Watt University;
- Imperial College London
- John Innes Centre
- Medical Research Council
- Swansea University
- University of Cambridge;
- University of Glasgow
- University of Leeds
- University of Liverpool;
- University of West London
- University of York;
- 17 more »
- « less
-
Field
-
, delivering tested methods, and creating algorithms to expand MMFM capabilities across domains like cardiology, geo-intelligence, and language communication. The postholder will help lead a project work package
-
Learning, Algorithms, Noise Handling (Error Correction/Mitigation), and Verification. These roles are part of the Quantum Software Lab (QSL, link: https://www.quantumsoftwarelab.com ), in collaboration with
-
aims to develop formal frameworks and algorithms for eliciting, aggregating, and analysing stakeholder preferences over risk and safety in AI systems. The Research Assistant will support the development
-
and evaluation. The post holder will take a leading role in advancing theoretical and algorithmic research in the domain of probabilistic preference aggregation, contribute to the design and analysis
-
computational frameworks that combine 4D point cloud data, geospatial analysis, and advanced ML/DL algorithms. Integrate dynamic environmental datasets into immersive and interactive prototypes for scenario
-
, including: Robot Learning: Creating algorithms that empower robots to learn autonomously from interactions and adjust to new tasks. Manipulation: Enhancing techniques for precise and adaptable object handling
-
Role Description Department We are seeking an experienced mathematical modeller. Our group is developing a range of advanced genetics-based methods for controlling mosquito-borne diseases, based
-
work closely with lab members but with a focus on EV-associated fungal proteins. They will assist in protocol and technique development and use reverse genetics for functional analysis of EV candidates
-
project aims to address the current limitations of traditional frame-based sensors and associated processing pipelines with a new family of algorithmic architectures that mimic more closely the behaviours
-
reverse genetics for functional analysis of EV candidates. This project will require a strong molecular biology background, knowledge in AI-based protein structural prediction, in vitro expression systems