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Postdoc in Large Scale Audio Machine Learning

Technical University of Denmark - DTU Compute

Location: Lyngby - Denmark
Salary: Not Specified
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 31st October 2018
Closes: 18th November 2018

DTU Compute’s Section for Cognitive Systems, invites applications for appointments as postdoc. The postdoc position is funded by a national scale citizen science project. The project is a collaboration also including University of Copenhagen, the Danish Agency for Science and Higher Education, University of Southern Denmark. The ambitious aim of the citizen science project is to map the entire Danish soundscape and to analyze a whole nations’ sound-topography. The project will collect data from the general public and create a detailed database of sounds which will be used to answer research questions in the field of natural, health, socioeconomic and social sciences, as well as used for technology development. The position is available immediately or according to mutual agreement. The postdoc position is initially available for one year. 

The Section for Cognitive Systems is a lively and research oriented group of scientists and support staff with a shared interest in information processing in man and computer, and a particular focus on the signals they exchange - audio, imagery, behavior – and the opportunities these signals offer for modeling and engineering of cognitive systems. 

Our department DTU Compute is an internationally unique academic environment spanning the disciplines mathematics, statistics and computer science. At the same time we are an engineering department covering informatics and communication technologies (ICT) in their broadest sense. Finally, we play a major role in addressing the societal challenges of the digital society where ICT is a part of every industry, service, and human endeavor.

Responsibilities and tasks

Your main tasks will be to design, develop and deploy a machine learning service for real-time analysis and assisted tagging of audio recordings. Your work will focus on deep neural network models for audio analysis (e.g., segmentation, categorisation and grouping). You will also be responsible for software making the machine learning pipeline available to our collaborators, including commercial partners supplying the production system. Your research will include the design and evaluation of the real-time machine learning models (both in the lab and in live production systems) along with statistical analysis of the gathered data together with our collaborators. 

Qualifications

General: 

  • A PhD in machine learning or a related field (e.g., data science, signal processing, computer vision).
  • We expect a track record of presentation and publication of research results in quality journals/conferences.
  • Experience in undertaking independent research.
  • Excellent communication skills (oral and written English), including public presentations and ability to communicate complex concepts concisely.
  • Excellent interpersonal skills including team working and a collegiate approach.
  • Experience and interest in collaborative and cross-disciplinary research. 

Specific qualifications needed: 

  • Theoretical and practical knowledge of modern machine learning (including deep neural networks) with application to multimedia.
  • Experience in programming and software development (using Python/C++/Java or similar) and web technologies (such as Rest APIs, Flask, JavaScript and Html).

Application procedure

To apply, please read the full job advertisement at www.career.dtu.dk.

Application deadline: 18 November 2018. 

DTU is a technical university providing internationally leading research, education, innovation and scientific advice. Our staff of 6,000 advance science and technology to create innovative solutions that meet the demands of society, and our 11,200 students are being educated to address the technological challenges of the future. DTU is an independent academic university collaborating globally with business, industry, government and public agencies.  

   
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