23 machine-learning-"https:"-"https:"-"https:"-"https:"-"U.S"-"TCAT-Dickson" PhD positions at Newcastle University
Sort by
Refine Your Search
-
reservoirs. By embedding governing equations and boundary conditions directly into machine-learning models, the project aims to enable efficient exploration of high-dimensional parameter spaces without
-
EPSRC ReNU+ CDT PhD Studentship: Physics-informed machine learning for deep geothermal systems under uncertainty. Award Summary 100% fees covered, and a minimum tax-free annual living allowance
-
PhD studentship: Adaptive Logic-Based Machine Learning Grids Award Summary 100% fees covered, and a minimum tax-free annual living allowance of £20,780 (2025/26 UKRI rate). Additional project costs
-
an adaptable Machine Learning (ML) hardware architecture to solve Artificial Intelligence (AI) classification tasks using Internet of Things (IoT) sensor data. This will be a small system-on-chip designed
-
PhD Studentships in Statistics, Data Science and Machine Learning Award Summary 100% home fees covered, and a minimum tax-free annual living allowance of £20,780 (2025/26 UKRI rate). Overview The
-
PhD studentship in Machine Learning-Based MIMO Radar Sensing for All-Weather Perception in Autonomous Vehicles Award Summary 100% fees covered, and a minimum tax-free annual living allowance
-
/10.1021/acs.jpcb.4c01558 ], but they lack accuracy for predictive modelling. Transferable machine learning potentials, like MACE-OFF [https://doi.org/10.1021/jacs.4c07099 ], effectively achieve quantum
-
prediction, a machine-learning surrogate model based on Gaussian process regression will be developed and trained using datasets generated by the high-fidelity numerical solver. The surrogate will emulate key
-
equations to simulate pollutant transport, mixing and biochemical processes. To enable rapid prediction, a machine-learning surrogate model based on Gaussian process regression will be developed and trained
-
programmed in advance. If anything changes, it may fail. This project explores how to build more adaptable systems using vision-language-action (VLA ) models. These combine computer vision (to see), natural