Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- United Kingdom
- Netherlands
- Germany
- Sweden
- Norway
- France
- Denmark
- Portugal
- Spain
- Australia
- Belgium
- Finland
- United Arab Emirates
- Canada
- Switzerland
- China
- Singapore
- Austria
- Estonia
- Greece
- Italy
- Morocco
- Poland
- Hong Kong
- India
- New Zealand
- Ireland
- Luxembourg
- Malta
- Romania
- Saudi Arabia
- 22 more »
- « less
-
Program
-
Field
-
the validation roadmap and Center’s priorities. Establishes timelines and strategies for the validation of different algorithms. Works with ML and engineering teams to develop and manage pipelines for continuous
-
. Design and implement multimodal unlearning techniques to address bias and privacy concerns. Evaluate the generalisability of multimodal learning across different socio-contexts. Validate the proposed
-
unit and then pre-processed data used as the input of the deep learning algorithm. The research will employ the SafeML tool (a novel open-source safety monitoring tool) to measure the statistical
-
scheduling to help make offshore wind farms a reality. Job description This post-doctoral position focuses on developing fundamental algorithmic advances for dynamic planning and scheduling in multi-objective
-
simulations are plagued by the same slow relaxational dynamics. Through collaboration across Engineering, Statistics and Chemistry, this project will develop state-of-the-art simulation algorithms to circumvent
-
health. Policymakers allocate limited testing and surveillance resources across different locations, aiming to maximise the information gained about disease prevalence and incidence. This project will
-
to be fast in practice, but the framework of worst-case analysis is unable to explain this observation. Different analysis frameworks have been proposed to explain the good performance of the algorithm
-
-of-the-art practical algorithms for real-world problems. This creates a very special environment, where we do not only conduct in-depth research on different theoretical and applied topics, but where different
-
be working primarily with scientific machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields
-
sensors systems and UAVs at different scales. In particular, we will combine borehole and surface GPR as well as small-scale EMI measurements with root and shoot observations in controlled experiments