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Position: Research Fellow in Bayesian Machine Learning for Psychoacoustic Modelling
Institution: University of Southampton
Location: Southampton, Hampshire, United Kingdom
Duties: 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 Bayesian deep learning. The position is available on a fixed term basis for 36 months due to funding restrictions
Requirements: 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: Bayesian deep learning; Development of custom modules using GPU-accelerated APIs for deep learning (e.g., Pytorch); and; Development of bespoke data loaders and pipelines
   
Text: Research Fellow in Bayesian Machine Learning for Psychoacoustic Modelling Agents, Interaction & Complexity Location: Highfield Campus Salary: £34,980 to £41,732 Per annum Full Time Fixed Term (36 months) Closing Date: Wednesday 24 April 2024 Interview Date: To be confirmed Reference: 2534723FP-R Research Fellow in Bayesian Deep Learning 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 Bayesian deep learning. The position is available on a fixed term basis for 36 months due to funding restrictions. You will be working under the direction of Dr Christine Evers on the EPSRC research programme “Challenges in Immersive Audio Technology (CIAT)” ( https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/X032981/1 ) conducted in collaboration between King’s College London, University of Surrey, and University of Southampton. You will address fundamental research questions surrounding the complexity, generalisability, and interpretability of Bayesian models. You will apply your research outputs to audio data with the aim to predict sound field attributes that are perceptually relevant to human listeners. 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: Bayesian deep learning; Development of custom modules using GPU-accelerated APIs for deep learning (e.g., Pytorch); and Development of bespoke data loaders and pipelines. 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 external project partners 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 between the University of Southampton, King’s College, and University of Surrey, as well as external, international project partners (e.g., Stanford CCRMA, USA). Opportunities to travel, e.g., for international conferences and research visits hosted by 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. 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: c.evers@soton.ac.uk
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