Are you keen to pioneer machine learning models that address the challenges of robot perception? We are recruiting a research fellow who will work amongst our dynamic team on an exciting, EPSRC-funded research project on “Active Audition for Robots (ActivATOR)”.
ActivATOR will develop novel machine learning models that enable robots to leverage the motion of their own bodies (‘egomotion’) to make sense of acoustic environments (e.g., shopping malls). Located at the intersection of machine learning, robotics, and acoustic signal processing, the project will bring together a highly interdisciplinary team of researchers, industry partners, and external academic collaborators.
We are seeking applications for a research fellow, available on a fixed term basis for 18 months due to funding restrictions. As part of your role, you will:
- Develop novel methods for probabilistic deep learning that will enable robots to make sense of life in sound under motion.
- Publish your findings at top-tier venues.
- Disseminate your research findings at national and international workshops and conferences.
- Collaborate with internal and external researchers to broaden the scope of your research.
- Liaise with our industry partners to ensure commercial impact of your research.
- Design and participate in engagement activities with the public, policymakers and key stakeholders to ensure societal benefit of your research.
You will benefit from:
- Extensive opportunities for collaboration with external project partners.
- Opportunities to travel, e.g., for international conferences and research visits hosted by project partners.
- Access to state-of-the-art research facilities, including dedicated laboratory space and robotic systems.
- 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.
We are seeking candidates with:
- A Ph.D. (either awarded or nearing completion) or equivalent professional qualification and experience in Computer Science, Engineering or a related field.
- Demonstrable experience in deep learning, evidenced by publications in top-tier conferences or journals.
- Experience in deploying deep learning models for audio, visual, or audio-visual processing.
- Excellent programming skills (e.g., TensorFlow, PyTorch).
- Proficiency in data analytics, pre-processing, and feature engineering.
- Experience with the Robot Operating System (ROS) is desirable but not essential.
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.
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).
The University of Southampton holds a Silver Athena Swan Award, offering a generous maternity policy; onsite childcare facilities; childcare vouchers scheme; contributory pension scheme; generous holiday entitlement; subsidised health and fitness facilities; cycle to work scheme; and a range of discounts. The University of Southampton is committed to sustainability and has recently been awarded the Platinum EcoAward.
Informal enquiries can be made to Dr Christine Evers, Associate Professor: email@example.com