219 machine-learning-"https:"-"https:"-"https:"-"https:"-"UCL"-"UCL" PhD positions in United Kingdom
Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- University of Nottingham
- Cranfield University
- The University of Manchester
- Newcastle University
- University of Exeter
- University of Warwick
- University of Birmingham
- University of Surrey
- Loughborough University;
- UNIVERSITY OF VIENNA
- University of Plymouth
- Manchester Metropolitan University;
- Northeastern University London
- University of Exeter;
- University of Nottingham;
- University of Surrey;
- ;
- Swansea University
- University of Birmingham;
- University of East Anglia
- University of East Anglia;
- University of Leeds
- University of Sheffield
- Imperial College London
- Manchester Metropolitan University
- The University of Edinburgh;
- University of Bradford;
- University of Bristol
- University of Liverpool
- University of Liverpool;
- University of Oxford
- University of Westminster;
- Cardiff University
- Cardiff University;
- City St George’s, University of London
- De Montfort University;
- Harper Adams University
- Imperial College London;
- King's College London
- King's College London;
- London School of Economics and Political Science;
- Middlesex University;
- Midlands Graduate School Doctoral Training Partnership
- Newcastle University;
- Swansea University;
- The Francis Crick Institute
- The Open University
- The Open University;
- The University of Manchester;
- UNIVERSITY OF MELBOURNE
- UWE, Bristol
- UWE, Bristol;
- Ulster University
- University of Bath;
- University of Cambridge
- University of Cambridge;
- University of Essex
- University of Greenwich
- University of Lincoln
- University of Newcastle
- University of Oxford;
- University of Southampton
- University of Warwick;
- University of York;
- 54 more »
- « less
-
Field
-
with NEOM, one of the world’s largest ecological restoration programmes, the project will develop machine-learning approaches to analyse satellite observations of vegetation change and evaluate large
-
, machine learning, and photonics. Be part of a multidisciplinary research team spanning science and engineering. Access state-of-the-art laboratories and high-performance computing facilities. Gain
-
harvesting recent breakthroughs in Machine Learning (ML) and analytical modelling. Specifically, this project seeks to quantify key performance metrics and create powerful adaptive ML-driven management methods
-
framework integrating physics-informed machine learning, scenario generation, and human-in-the-loop preference-based reinforcement learning to prioritise climate-robust and equity-aligned interventions
-
programming (e.g., Python/C++), machine learning frameworks, or robotics software environments such as ROS. You are motivated to work in a multi-disciplinary research environment combining engineering, AI, and
-
training programme at the start of the PhD to develop skills in areas such as programming, data analysis, machine learning and signal processing. This will provide the technical foundation required to work
-
modelling and oomph-lib for continuum mechanics simulations, enabling the integration of discrete and finite element methods. Coupled with machine learning techniques, this approach will address the complex
-
industries like pharmaceuticals, food processing, and construction, the project may also incorporate machine learning methods for model calibration and optimisation, driving more sustainable material handling
-
/physics or any related discipline. This is a largely experimental research project based at the University of Nottingham, with some aspects of material modelling and development of machine learning to aid
-
for operational decision‑making, interactive design, or control‑in‑the‑loop visualisation. Machine‑learning surrogates offer speed, yet purely data‑driven models often extrapolate poorly and may violate physical