The four-year PhD programme includes in its first year intensive courses that provide a comprehensive introduction to theoretical and systems neuroscience and machine learning (see Teaching). Multidisciplinary training in other areas of neuroscience is also available. We offer a supportive and interdisciplinary environment with close links to the Sainsbury Wellcome Centre for Neural Circuits and Behaviours (SWC) and the ELLIS Unit at UCL. Students are strongly encouraged to work and interact closely with peers and faculty at SWC and the ELLIS Unit to benefit from this uniquely multidisciplinary research environment. Projects involving collaboration with researchers at and/or external to UCL are welcome. For details see programme structure.

Selection for third-cycle studies is based on the student's potential to profit from such studies. The assessment of potential is made primarily on the basis of academic results from the first and second cycle. Consideration will be given to good collaborative skills, drive and independence, and how the applicant, through his or her experience and skills, is deemed to have the abilities necessary for successfully completing the third cycle programme. Technology and society is a third-cycle subject that encompasses multidisciplinary and interdisciplinary studies of technology's role, interplay and importance in different sectors of society. The position is linked to a highly interdisciplinary research project that explores how AI transparency relates to consumer trust.

The University of Exeter's College of Engineering, Mathematics and Physical Sciences is inviting applications for a fully-funded PhD studentship to commence in January 2022 or as soon as possible thereafter. The studentship will cover Home tuition fees plus an annual tax-free stipend of at least £15,609 for 3.5 years full-time, or pro rata for part-time study. This College studentship is open to UK and Irish nationals, who if successful in their application will receive a full studentship including payment of university tuition fees at the home fees rate. Uncertainty Quantification (UQ) for engineering models is a rapidly growing field with numerous exciting applications. However, the current best-performing algorithms for quantifying the uncertainty through Markov Chain Monte Carlo (MCMC) rely on computing a gradient that is typically not readily available for complex engineering models.

Applications are invited for a PhD studentship, to be undertaken at Imperial College London (Electrical and Electronic Engineering Department). This studentship will form part of a newly established International Centre for Spatial Computational Learning http://spatialml.net, and a supervisory team will be allocated based on the student's interest from the Imperial College supervisors participating in the Centre. This is an exciting cutting-edge project involving close collaboration between Imperial College (UK), the University of California Los Angeles (USA), the University of Toronto (Canada), and the University of Southampton (UK). The successful candidate will be based at Imperial but will have the opportunity to travel frequently to America to attend research meetings and for a placement period at either UCLA or Toronto. Traditional deep learning has been based on the idea of large-scale linear arithmetic units, effectively computing matrix-matrix multiplication, combined with nonlinear activation functions.

In collaboration with the European Institute of Innovation and Technology (EIT) and Bosch Automotive Service Solutions Ltd, the School of Mathematics, Computer Science & Engineering is offering a PhD studentship. The studentship falls under the new EIT-Digital Industrial Artificial Intelligence Doctoral Programme at City, University of London. The studentship consists of a full fee waiver and a stipend of £18K per year, for four years. As part of their studies and training, students will spend time at City, University of London, EIT-Digital London Co-Location Centre and Bosch workshop in Stockport (Greater Manchester). In addition, over the 4 years of study, the PhD will spend between 3 and 6 months abroad to enrich their research experience, for which a supplementary budget is available.

In collaboration with the European Institute of Innovation and Technology (EIT) and Delta Capita Ltd, the School of Mathematics, Computer Science & Engineering is offering a PhD studentship. The studentship falls under the new EIT-Digital Industrial Artificial Intelligence Doctoral Programme at City, University of London. The studentship consists of a full fee waiver and a stipend of £18K per year, for four years. As part of their studies and training, students will spend time at City, University of London, EIT-Digital London Co-Location Centre and Delta Capita Ltd premises. In addition, over the 4 years of study, the PhD will spend between 3 and 6 months abroad to enrich their research experience, for which a supplementary budget is available.

A funded PhD position is available at the Computer Science Department, School of Science, Loughborough University, UK, on the topic of the evolution of lifelong learning in neural networks. The aim is to develop new neuroevolution algorithms for lifelong learning. The objectives are to devise machine learning systems that autonomously adapt to changing conditions such as variation of the data distribution, variation of the problem domain or parameters, with minimal human intervention. The approach will use neuroevolution, neuromodulation, and other methodologies to continuously discover and update learning strategies, implement selective plasticity, and achieve continual learning. Application areas include a variety of automation and machine learning problems, e.g.

Project Description: Forensic facial reconstruction research is at the crossroads of art, medicine and science. It can be employed in the context of forensic investigation and for creating three-dimensional portraits of people from the past, from dead bodies and ancient Egyptian mummies to digital animations [1]. Previously, forensic anatomists and artists have relied on manually creating a 3D face clay model or through using computerized 3D forensic facial reconstruction software to manually reconstruct the face [2]. The aim of this multi-disciplinary PhD project is to develop advanced machine-learning and image-processing methods to learn how to automatically and accurately predict a facial'photograph' of a person from their skull (reconstructed using MRI), which can contribute to advancing both forensic medicine and science [2] as well as surface shape reconstruction from photos [3]. Developing such predictive models could be made in a fully quantitative manner with statistical uncertainty bounds on the predicted facial features as well as dynamic morphing of the predicted face within these bounds to aid recognition.

The safety of critical maritime operations and the power output of wave energy converters can both be improved by measuring and predicting the shape and motion of sea waves. The aim of this project is extract information from monoscopic video footage of the sea's surface that enable its shape and motion to be modelled. The models will then be used to predict its future motion up to two minutes ahead. Making observations of the shapes of sea waves is difficult. We have been working with wave profiling radar, which is relatively expensive and difficult to install and run.