PhD Position in Hybrid AI for Machine Listening Applications

Catholic University of Leuven

Belgium

 
Acoustic data has ample perspective to glean useful insights about the monitoring context. Automating the interpretation of acoustic data streams and time-series data in general is a primary focus of the KU Leuven Advanced Integrated Sensing lab (ADVISE) research group which resides in the Faculty of Engineering Technology at KU Leuven Geel campus. A wide range of real-life applications are being targeted such as indoor monitoring of domestic sounds and human movements, precision livestock farming, condition-based monitoring of assets and materials. In total a group of 7 PhD researchers and 1 post-doc are currently involved in this research track which is being coordinated by Prof. Peter Karsmakers (https://www.kuleuven.be/wieiswie/en/person/00047893). This group is part of the Computer Science Department of the KU Leuven and has a strong link with the DTAI research team (https://dtai.cs.kuleuven.be).

Project

Machine learning and more specifically deep learning has provided compelling solutions for automated interpretation of acoustic signals in a broad range of applications. However, for an appropriate performance large amounts of labeled training examples and substantial computational resources are required, which are difficult to obtain in many complex real-world domains.

Among AI experts there is a growing consensus that, in order to make AI truly successful, it is necessary to integrate data-driven (“learning”) and knowledge-driven (“reasoning”) approaches. Essentially, the knowledge-driven approach focuses on advanced reasoning with complex knowledge which includes commonsense knowledge, and information about domain dynamics and context, obtained from humans or prior experience. Such knowledge needs to explicitly be programmed but offer explainability, transparency, provenance and reuse. Using such an approach directly on time-series data (e.g. from a microphone) is challenging. Data-driven methods like deep learning are better suited to discover patterns in such noisy sensor data. When capturing these patterns in some form of symbolic formalism advanced reasoning approaches could be applied leading to a solution that benefits the best of both worlds.  Having this symbolic layer in-between both approaches enables adding prior knowledge to the modeling task which is expected to lower the amount of data needed and giving actionable insights.

The DTAI research team has strong knowledge and is investigating novel concepts on how to bridge the gap between the two approaches. The ADVISE team has strong knowledge in building solutions for machine listening applications. 

In short in this PhD research DTAI-ADVISE wants to investigate the use of hybrid AI techniques within the field of machine listening and more specifically on two use-cases: indoor human activity monitoring and smart maintenance. 

The researcher will mainly be active at the premises in Geel campus with regular meetings at the campus in Heverlee.

Profile

If you recognize yourself in the story below, then you have the profile that fits the project and the research group.
  • I have a master’s degree in engineering, computer science, physics or mathematics and performed above average in comparison to my peers.
  • I’m proficient in written and spoken English.
  • During my courses or prior professional activities, I have gathered some basic experience with signal processing, machine learning, and/or I have a profound interest in these topics.      
  • As a PhD researcher I perform research in a structured and scientifically sound manner. I read technical papers, understand the nuances between different theories and implement and improve methodologies myself. 
  • Based on interactions and discussions with my supervisors and my team colleagues, I set up and update a plan of approach for the upcoming 1 to 3 months to work towards my research goals. I work with a sufficient degree of independence to follow my plan and achieve the goals. I indicate timely when deviations of the plan are required, if goals cannot be met or if I want to discuss intermediate results or issues.
  • In frequent reporting, varying between weekly to monthly, I show the results that I have obtained, and I give a well-founded interpretation of those results. I iterate on my work and my approach based on the feedback of my supervisors which steer the direction of my research.
  • I value being part of a large research community and I am eager to learn how academic research can be linked to industrial innovation roadmaps.
  • During my PhD I want to grow towards following up the project that I am involved in and representing the research group on project meetings or conferences. I see those events as an occasion to disseminate my work to an audience of international experts and research colleagues, and to learn on the larger context of my research and the research project.
  • Experience with scientific data processing software such as Python is a plus.

Offer

  • A remuneration package competitive with industry standards in Belgium, a country with a high quality of life and excellent health care system.
  • A chance to pursue a PhD in Engineering, typically a 4 year trajectory, in a stimulating and ambitious research environment.  
  • Ample opportunity to develop yourself in both a scientific or an industrial direction, besides opportunities provided by the research group, further doctoral training for PhD candidates is provided in the framework of the Leuven Arenberg Doctoral School (https://set.kuleuven.be/phd), known for its strong focus on both future scientists and scientifically trained professionals who will valorise their doctoral expertise and competences in a non-academic context. More information on the training opportunities can be found on the following link: https://set.kuleuven.be/phd/dopl/whytraining. 

Interested?

To apply for this position, please follow the application tool and enclose:

1. full CV – mandatory

2. motivation letter – mandatory

3. full list of credits and grades of both BSc and MSc degrees (as well as their transcription to English if possible) – mandatory (when you haven’t finished your degree yet, just provide us with the partial list of already available credits and grades)

4. proof of English proficiency (TOEFL, IELTS, …) - if available

5. two reference letters - if available

6. an English version of MSc, or of a recent publication or assignment - if available

For more information please contact Peter Karsmakers, by sending an e-mail to peter.karsmakers@kuleuven.be and mention Hybrid AI Vacancy in the title.

You can apply for this job no later than July 20, 2020 via the
KU Leuven seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments. If you have any questions relating to accessibility or support, please contact us at diversiteit.HR@kuleuven.be.
  • Employment percentage: Voltijds
  • Location: Geel
  • Apply before: July 20, 2020
  • Tags: Industriële Ingenieurswetenschappen


In your application, please refer to Polytechnicpositions.com

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