Sort by
Refine Your Search
-
Listed
-
Employer
- University of Oxford
- AALTO UNIVERSITY
- Durham University
- Heriot Watt University
- ;
- DURHAM UNIVERSITY
- KINGS COLLEGE LONDON
- Imperial College London
- King's College London
- City University London
- Heriot-Watt University;
- Swansea University
- UNIVERSITY OF VIENNA
- University of Cambridge
- University of Cambridge;
- 5 more »
- « less
-
Field
-
into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and applications (e.g. natural language processing, multivariate time-series data
-
, 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
-
EPSRC-funded project, MAPFSI that will be focused on developing experimentally-validated computational algorithms for fluid-structure interaction problems including multiphysics effect of electromagnetism
-
Responsibilities Develop suitable algorithmic methods for live and real-time analysis of synchronous and asynchronous data. Write research reports and publications. Analyse and interpret the results of own research
-
. Research Environment The project is in collaboration with two partners: (i) IDCOM at the University of Edinburgh, which develops theory, algorithms and hardware for the next generation of signal processing
-
aims to optimize the operations (serving) of AI by developing algorithms that manage compute, network, and storage resources in a carbon-efficient way while supporting long-term benefits
-
for Artificial Intelligence (FCAI), ELLIS Institute Finland, and Aalto University House of AI, invites applications for multiple postdoctoral positions. Our team works actively to develop intelligent robotic
-
algorithms. The research focuses on wind energy applications, creating a compelling sustainability narrative: developing more efficient computational methods to optimize wind farm performance, which in turn
-
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