29 machine-learning-"The-University-of-Southampton" PhD positions

PhD Studentship: AI-based multi-modal 3D environment model reconstruction

model reconstruction. Supervisor Associate Professor Hansung Kim Vision, Learning and Control Research Group School of Electronics and Computer Science, University of Southampton http://www.3dkim.com/Eng

PhD Studentship: General Purpose Machine Learning Tool-Kit for Bragg Coherent Diffraction Imaging

PhD Supervisor: Dr Marcus Newton Supervisory Team: Dr Marcus Newton, Dr Dan Porter, Prof Steve Collins, Prof Paul Quinn Project description: The University of Southampton is expanding its PhD

PhD Studentship: Understanding Deep Learning

students, including Bursaries and Scholarships.  For more information please visit PhD Scholarships | Doctoral College | University of Southampton Funding will be awarded on a rolling basis, so apply early

PhD Studentship: Skill Acquisition in Lifelong Robot Learning with Large Language Models

University Guide 2022) within the University of Southampton which is ranked in the top 1% of universities worldwide. The successful candidate must have a strong background in machine learning. Prior knowledge

PhD Studentship - Large Area 2D Semiconductor Platforms

and device design domains. In addition to field specific skills, the University of Southampton training and mentoring programmes will provide training in report writing, project management, time

Research Assistant/Associate in Efficient Machine Learning Systems for Earth Observation (Fixed Term)

Applications are invited for a Research Assistant or Research Associate to work on efficient machine-learning systems for earth observation. The post holder will be part of the Computer Architecture

PhD Studentship: Privacy Attacks and Defences in Federated Learning Systems

privacy-preserving technology in Machine Learning. Despite its advancements, FL systems are not immune to privacy breaches due to the inherent memorisation capabilities of deep learning models

PhD Studentship: Using machine learning to evaluate atomic force microscopy nanoindentation data

Project title: Using machine learning to evaluate atomic force microscopy nanoindentation data Supervisory Team: Dr Martin Stolz, Dr Sasan Mahmoodi Project description: The University of Southampton

PhD Studentship: Nonlinear Aerostructural Optimisation of Boundary Layer Ingesting Fan

our high-fidelity multidisciplinary design optimisation (MDO) framework for the design of BLI fans. The proposed MDO is based on adjoint aerostructural optimisation, augmented with machine learning

PhD Studentship: High-bandwidth Implantable Brain Processing: From Theory to Practice

and collaborate with a strong multidisciplinary team of academics at University of Southampton and experts from University College London. Person specifications: Master degree in machine learning