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Position: Postdoctoral Research Assistant - Computational Sound Scene Analysis
Institution: Queen Mary University of London
Department: School of Electronic Engineering & Computer Science
Location: London, United Kingdom
Duties: The school of Electronic Engineering and Computer Science has 2 year funding for a Post-Doctoral Research Assistant as part of the BBSRC-funded project “Machine learning for birdsong learning”. The responsibilities of the role are to investigate, develop and evaluate novel digital signal processing and machine learning technologies for assessing the acoustic similarity of animal sounds, making use of behavioral data generated/used within the project as well as existing datasets. The work will include helping to facilitate a public data “challenge” for machine learning, as well as active collaboration with researchers working on animal behavior and animal communication
Requirements: The ideal candidate will have a PhD in Computer Science, Electrical/Electronic Engineering or in a relevant field, with research experience in one or more of the following areas: Digital Signal Processing, Machine Learning, Audio/Acoustics or equivalent. The candidate will have a strong research track record with publications in high-quality journals and conference proceedings. The successful candidate will have programming proficiency in one or more of: Python, Java, Matlab, C/C++ and will have demonstrated the ability to work collaboratively and independently. The ideal candidate will have demonstrable experience working with modern deep learning programming frameworks
   
Text: Postdoctoral Research Assistant - QMUL17846 Department: School of Electronic Engineering & Computer Science Salary: £33,615 - £37,411 per annum (Grade 4) Reference: QMUL17846 Date posted: 12-Mar-2019 Closing date: 11-Apr-2019 Job profile Apply online Overview: The school of Electronic Engineering and Computer Science at Queen Mary University of London has 2 year funding for a Post-Doctoral Research Assistant (PDRA) on computational sound scene analysis, as part of the BBSRC-funded project “Machine learning for birdsong learning”. The responsibilities of the role are to investigate, develop and evaluate novel digital signal processing and machine learning technologies for assessing the acoustic similarity of animal sounds, making use of behavioral data generated/used within the project as well as existing datasets. The work will include helping to facilitate a public data “challenge” for machine learning, as well as active collaboration with researchers working on animal behavior and animal communication. This post is based in the Centre for Digital Music (C4DM) and Machine Listening Lab (MLLab) of Queen Mary University of London. C4DM is a world-leading multidisciplinary research group in the field of Digital Music & Audio Technology; MLLab is a focused group working on methods for automatic understanding of many types of sound, from music through to animal sound, speech and everyday soundscapes. Both groups are part of the School of Electronic Engineering and Computer Science (EECS). Details about the School can be found at http://www.eecs.qmul.ac.uk; details about C4DM at http://c4dm.eecs.qmul.ac.uk; and details about the MLLab at http://machine-listening.eecs.qmul.ac.uk/ The ideal candidate will have a PhD in Computer Science, Electrical/Electronic Engineering or in a relevant field, with research experience in one or more of the following areas: Digital Signal Processing, Machine Learning, Audio/Acoustics or equivalent. The candidate will have a strong research track record with publications in high-quality journals and conference proceedings. Research experience in audio signal processing and/or computational sound scene analysis is desirable. The successful candidate will have programming proficiency in one or more of: Python, Java, Matlab, C/C++ and will have demonstrated the ability to work collaboratively and independently. The ideal candidate will have demonstrable experience working with modern deep learning programming frameworks. The post is full time, fixed term appointment for 2 years (or until 30 June 2021 whichever is the shorter), with an expected start date of 01 June 2019 or as soon as feasible after this date. Starting salary will be in the range of £33,615 - £37,411 per annum, inclusive of London Allowance. Benefits include 30 days annual leave, pension scheme and interest-free season ticket loan. Candidates must be able to demonstrate their eligibility to work in the UK in accordance with the Immigration, Asylum and Nationality Act 2006. Where required this may include entry clearance or continued leave to remain under the Points Based Immigration Scheme. Informal enquiries should be addressed to Dan Stowell at dan.stowell@qmul.ac.uk Details about the school can be found at www.eecs.qmul.ac.uk To apply, please visit the Human Resources website on http://www.jobs.qmul.ac.uk and search for reference QMUL17846 Candidates are kindly requested to upload documents totalling no more than 10 pages. Please note large documents, e.g. PhD thesis/Research papers, are not forwarded to the interview panel. The closing date for applications is 11th April 2019. Interviews are expected to be held shortly thereafter. Valuing Diversity & Committed to Equality QMUL is proud to be a London Living Wage Employer Apply online
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