Research Fellow in Deep Representation Learning

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We are seeking applications for a research fellow at the University of Southampton within the Vision, Learning and Control (VLC) Group in Electronics and Computer Science, to work in the area of deep representation learning for audio data. The position is available on a fixed term basis for 18 months due to funding restrictions, with the possibility to extend the contract for another 18 months.

You will be working on the EPSRC research programme “Active Audition for Robots (Activator)” (https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/W017466/1 ) under the direction of the Principal Investigator, Dr Christine Evers. The aim of the project is to enable robots to leverage the motion of their own bodies (‘ego-motion’) in order to make sense of life in sound. This vacancy will focus on underpinning research in deep learning, addressing fundamental challenges surrounding the disentanglement of learnt representations. As part of your role, you will:

• Publish and disseminate your findings at top-tier venues (e.g., NeurIPS, ICLR, ICML, IEEE Transactions);
• Collaborate with our internal and external partners (e.g., National Oceanography Centre) to broaden the scope of your research;
• Liaise with our industry partners to ensure commercial impact of your research; and 
• Develop and participate in activities for engagement with the public, policymakers and key stakeholders.

You will benefit from:

• Extensive opportunities for collaboration with our project partners.
• Access to state-of-the-art research facilities, including a high-performance compute cluster that is specifically aimed at workloads requiring large amounts of GPU memory or long run times.
• A vibrant, diverse, and inclusive academic community.
• Opportunities for professional development and career growth, e.g., mentorship of PhD students, development of funding applications, involvement in teaching activities.
• Opportunities for career development, including contributions to teaching, generation of future funding bids, and co-supervision of PhD, taught postgraduate and undergraduate projects.

We are seeking candidates with a Ph.D. (either awarded or nearing completion) or equivalent professional qualification and experience in Machine Learning, Statistics, or a related field, who have in-depth knowledge in and demonstrable experience with:

• Recent deep learning techniques, including, e.g., diffusion models and attentional models.
• Development of custom modules using GPU-accelerated APIs for deep learning (e.g., Pytorch); and
• Development of bespoke data loaders and pipelines.

Ranked in the top 1% of universities globally and among the UK's top 20 for research, the University of Southampton has an international reputation for its research, teaching and enterprise activities. The post will be held in the School of Electronics and Computer Science (ECS) , a friendly and supportive environment that facilitates high-impact, multi-disciplinary research, education, training, and outreach. ECS holds an Athena SWAN bronze award in recognition of its continued commitment to improving equality for women in science and engineering. 

We will give due consideration to applicants who wish to work flexibly including part-time, and to those who have taken a career break. We have a range of staff development programmes and a unique mentoring and wellbeing scheme (https://www.ecs.soton.ac.uk/workinghere ). 

Informal enquiries can be made to Dr Christine Evers, Associate Professor: [email protected]

Applications for Research Fellow positions will be considered from candidates who are working towards or nearing completion of a relevant PhD qualification.  The title of Research Fellow will be applied upon successful completion of the PhD.  Prior to the qualification being awarded the title of Senior Research Assistant will be given.

We are committed to equality, diversity and inclusion and welcome applicants who support our mission of inclusivity.

Apply by midnight on the closing date. For assistance contact Recruitment on +44(0)2380 592750 or [email protected] quoting the job number.