Sort by
Refine Your Search
-
mechanisms such as neural networks-like or sensors that will be implemented in robots so that the origami can act while avoiding obstacles or sense chemical/optical gradients in the environment, even though
-
incorporating various friction models. You will conduct experiments with a physical rig to identify essential system parameters, friction characteristics, and actuator/sensor models. This process will not only
-
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
-
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
-
Dark Matter Search with ADMX and the Quantum Sensors for the Hidden Sector Collaboration
-
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
-
Deadline: 31 October 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
-
, including how to guarantee the properties of stability and constraint satisfaction while probing the system and learning a new model. This project aims to develop novel algorithms for the adaptive distributed
-
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