Research Fellow in Computational Ecoacoustics Ref 7983
School/department: Engineering and Informatics/ Informatics
Hours: Full time - considered up to a maximum of 100% FTE
Requests for flexible working options will be considered (subject to business need).
Contract: fixed term for 6 months
Reference: 7983
Salary: starting at £34,304 to £40,927 per annum, pro rata if part time
Placed on: 15 March 2022
Closing date: 29 March 2022. Applications must be received by midnight of the closing date.
Expected start date: 1 May 2022 or as soon as possible thereafter.
Job description
A full time, 6 month Postdoctoral Research Fellow position is available to work at the forefront of Computational Ecoacoustics as part of a pilot project investigating the potential for dynamical complexity metrics in the ecological assessment of natural soundscapes. The exploratory nature of this project offers the opportunity to have strong creative input, and – if successful - the project has the potential to develop into a major research endeavor in the future.
Context: Monitoring, understanding, and predicting the integrity of our planetary biosphere is the most critical sustainability issue of our time. The emerging science of ecoacoustics points to the exciting possibility that eavesdropping on ecosystems can provide a cost-effective solution. The soundscape is a highly dynamic pattern of acoustic energy, which emerges from the interaction of the sounds of organisms, geophysical and technological processes. Current approaches in computational ecoaoustics are extremely simple, precluding investigation of these rich spatio-temporal dynamics, and how they may relate to ecosystem health and integrity. However, emerging methods in complexity science point to exciting new possibilities.
Your Role: You will bring strong mathematics and computing skills and a rigorous, experimental approach to a multidisciplinary team of pioneering researchers across ecoacoustics, complexity science, neuroscience, AI and music to investigate the potential of new and emerging information-theoretic complexity measures as new acoustic ecological assessment tools for applied conservation.
Working on the EPSRC-funded pilot project “Toward a Measure of Soundscape Dynamical Acoustic Complexity using Causal Analysis and AI”, you will collaborate with the research team to carry out pioneering research and co-author high quality peer reviewed publications.
Situated within a world-leading research environment of the AI Research Group in the school of Engineering and Informatics at the University of Sussex, you will have ample with opportunities to engage with and contribute to a vibrant research environment nourished by activities of groups including the Sackler Centre for Consciousness Science, the Predictive Analytics Lab and the Sussex Humanities lab.
For an informal discussion of the post, please contact Alice Eldridge, Reader in Sonic Systems, in the first instance by email at: alicee@sussex.ac.uk
Applications should be accompanied by a full CV, a statement of research interests and aspirations (not more than 4 pages), and the names of three academic referees.
The University is committed to equality and valuing diversity, and applications are particularly welcomed from women and black and minority ethnic candidates, who are under-represented in academic posts in Science, Technology, Engineering, Medicine and Mathematics (STEMM) at Sussex.
Please note that this position may be subject to ATAS clearance if you require visa sponsorship.
The University of Sussex values the diversity of its staff and students and we welcome applicants from all backgrounds.
You can find out more about our values and our EDI Strategy, Inclusive Sussex, on our webpages.
Download the full job description and person specification (reference number 7983) (PDF XKB)
The University requires that work undertaken for the University is performed from the UK.
Visa Sponsorship Queries:
For VISA SPONSORSHIP
"This role has been assigned an eligible SOC code and meets the salary requirements for Skilled Worker Sponsorship. Please consult our Skilled Worker Visa information page for further information about Visa Sponsorship."
"This role has been assigned an eligible SOC code however to meet the minimum salary requirement this is dependent on the salary you are offered and/or whether you are eligible for tradeable points. If you require visa sponsorship from the University of Sussex to undertake this role please consult our Skilled Worker Visa information page for further information. If you want further advice on sponsorship eligibility please contact HRCompliance@sussex.ac.uk"
“Please note that this position may be subject to ATAS clearance if you require visa sponsorship.”
How to apply
Download our academic application form [DOC 199.50KB] and Personal Details and Equal Opportunities Form [DOC 119.50KB] and fill in all sections.
Email your completed application, and personal details and equal opportunities form, to enginfrecruitment@sussex.ac.uk
You should attach your application form and all documents to the email in PDF format (we are unable to accept applications as google.docs or .pages) and use the format job reference number / job title / your name in the subject line.
You can also send your application by post to Human Resources Division, Sussex House, University of Sussex, Falmer, Brighton, BN1 9RH.
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