39 machine-learning-phd "The Art Institutes" research jobs at UNIVERSITY OF SOUTHAMPTON in United Kingdom

Research Fellow - Efficient Machine Learning Systems

-funded project led by Dr. Jagmohan Chauhan in an exciting area of embedded machine learning. The post will be based at the University of Southampton. You will be working with Dr. Jagmohan Chauhan (PI), a

Research Fellow in Efficient Machine Learning Systems

collaborators and travel to academic conferences and project meetings to present the work. Successful candidates must hold (or close to completing) a PhD in a relevant subject. Knowledge and experience in

Research Fellow in Bayesian Machine Learning for Psychoacoustic Modelling

listeners. We are seeking candidates with a Ph.D. (either awarded or nearing completion) or equivalent professional qualification and experience in Machine Learning, Statistics, or a related field, who have

Research Fellow in Bayesian Machine Learning for Psychoacoustic Modelling

term basis for 36 months due to funding restrictions. As part of your role, you will: Develop novel Bayesian machine learning approaches for psychoacoustic modelling. Publish your findings at top-tier

Research Fellow, Vision, Learning and Control

in Python, experience of machine learning with scikit-learn and the ability to work collaboratively. This position is ideal for someone who has recently or is about to finish their PhD and is

Research Fellow in Deep Representation Learning

with a Ph.D. (either awarded or nearing completion) or equivalent professional qualification and experience in Machine Learning, Statistics, or a related field, who have in-depth knowledge in and

Research Fellow in Responsible AI

strategic investment by UKRI, and part of the School of Electronics and Computer Science. The role will involve a core focus on AI research (machine learning, multi-agent systems, causal AI, optimisation

Research Fellow in Drivers of Global Water and Sediment Dynamics

with a global sediment database and use remotely sensed and other geographical data with machine learning/Bayesian Modelling techniques to establish drivers of global sediment flux. They will use

Research Fellow in High-Performance Computing

constantly ingests data from many sources, generates potential adverse scenarios, models and labels the scenarios, and uses new and existing machine learning methods to build intelligent and proactive risk

Research Fellow in Event-triggered Modelling

constantly ingests data from many sources, generates potential adverse scenarios, models and labels the scenarios, and uses new and existing machine learning methods to build intelligent and proactive risk