We are seeking a highly motivated candidate to commence a PhD journey in the dynamic and rapidly growing field of quantum machine learning (QML), focusing on its application in energy system. Read more
Modelling of many modern applications leads to linear systems whose size is too large to allow the use of direct solvers. Thus, parallel solvers are becoming increasingly important in scientific computing. Read more
Assessment of engineering drawings can be a complex and lengthy task which causes delay in getting feedback to students. The provision of constructive, relevant and timely feedback is essential to student learning. Read more
Federated learning is a machine learning approach that enables multiple parties to collaborate in developing a shared model while safeguarding the distribution and privacy of their data. Read more
Project description. This PhD project aims to develop Machine Learning methods for Molecular Modeling with a particular focus on aspects relevant to dynamics preserving coarse-graining strategies. Read more
Quantum Machine Learning (QML) is an emerging field of research that leverages quantum computing to improve the classical machine learning approach to solve complex real-world problems. Read more
Optimisation problems are ubiquitous across many sectors. In a typical scenario, instances arrive in a continual stream and a solution needs to be quickly produced. Read more
These projects are open to students worldwide, but have no funding attached. Therefore, the successful applicant will be expected to fund tuition fees at the relevant level (home or international) and any applicable additional research costs. Read more
We develop probabilistic machine learning methods for helping other agents make better decisions, ultimately human agents in ongoing applications in science and engineering. Read more
Supervisory Team. Dr Felix Langfeldt; Prof Thomas Blumensath. Project description. Explore the forefront of Acoustical Engineering with this PhD opportunity at the University of Southampton. Read more
We are seeking a highly motivated and talented candidate for a self-funded PhD position in the field of Few-Shot Medical Segmentation using Deep Learning. Read more
With the development of deep learning approaches and convolutional neural networks (CNN) in particular, the task of recognising objects from an image has become associated with the ability to train a network using a large number of labelled images for each class of interest [He2016]. Read more
*Offer only available for the duration of your active subscription, and subject to change. You MUST claim your prize within 72 hours, if not we will redraw.
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