201 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" positions at University of Sheffield
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
physical systems. You will explore how the dynamic behaviour of nanomagnetic devices can be used to realise these KAN functions directly in hardware. Working with a combination of modelling, machine learning
-
developing a computational model that simulates blood flow for ICH patients. The research will exploit a powerful new approach — physics- informed neural networks (PINNs) — that combines machine learning with
-
PhD: Systematic Exploration of Robot Behaviours for Manufacturing Tasks to Automatically Discover Failure Scenarios EPSRC Centre for Doctoral Training in Machining, Assembly, and Digital Engineering
-
and language therapy in the areas of paediatrics/children/young people/learning disability. Other areas of expertise can also be considered. Other duties will include student engagement, student
-
photonic quantum computers with cloud access. QuiX Quantum is building Europe’s first universal photonic quantum computer using silicon nitride photonic chips for scalable operation. In the US, PsiQuantum is
-
that can use explanation as a core mechanism for learning and reasoning in natural language. To this end, he investigates the integration of neural and symbolic AI methods to enhance the robustness and
-
Application Deadline: Applications accepted all year round Details Self-driving laboratories (SDLs) combine the power of artificial intelligence (AI) and machine learning (ML), robotics, and automation
-
knowledge or skills are required, beyond general computer literacy, as all training will be provided. The project requires a highly motivated research associate, with excellent communication skills in French
-
AI-based diagnostics for fleet-based condition monitoring of electric vehicle motors using machine learning frameworks (S3.5-ELE-Panagiotou)
-
Physics based machine learning algorithm to assess the onset of amplitude modulation in wind turbine noise (with TNEI Group)