Sort by
Refine Your Search
-
at the University of Sheffield within the consortium is to lead nationally the development of quantum machine learning (QML) algorithms. The research will involve designing innovative QML approaches and collaborating
-
machine vision algorithms. The system will be designed with the physical constraints of remote fusion environments in mind, including radiation tolerance, restricted access, and the need for automation and
-
Deadline: 30 June 2025 Details This project aims to develop new algorithms for reinforcement learning from human feedback, to effectively solve complex reinforcement learning tasks without a predefined
-
the results of which would be used to enrich the available experimental data in order to develop a Design for Manufacture and Performance concept based on machine learning algorithms where the required
-
argon. The analysis of the ProtoDUNE data will help to validate calibration techniques and particle identification algorithms. The candidate should have a good knowledge of particle physics and experience
-
electromagnetic design. We will explore advanced topologies for mmwave metasurfaces, design novel reconfiguration mechanisms, and develop intelligent algorithms to optimize scattering characteristics in real-time
-
processing, data analysis, data-driven modelling, optimisation and computation algorithms, machine learning models and neural network structures, as well as strong skills and experiences in computational
-
booking the venue, ordering catering and audio-visual equipment, distributing papers, preparing agendas, taking minutes for a variety of meetings and following up on action points. Produce high quality
-
hardware-in-the-loop (HiL) techniques and ML algorithms for the accurate and on-time detection of faults, so that failures can be prevented by alerting the end-users and diagnosticians during periodical
-
. Assess the build quality of parts generated through control model algorithms. Validate that methodologies developed are transferrable between different LPBF platforms through evaluation of parts generated