PhD Position on Bayesian Machine Learning for Audio Processing

Department(s)

Electrical Engineering

Reference number

V36.5600

This research program is aimed at developing modern machine learning methods that lead to improved performance of audio processing algorithms (e.g., for hearing aids). In particular, our approach is to study computational models of learning and adaptation in brains and apply these ideas to the design of personalization of audio processing applications. Key areas of interest include Bayesian machine learning, probabilistic graphical models (factor graphs), blind source separation, computational neurosciences and signal processing. We develop our own toolbox (see http://forneylab.org) for Bayesian inference and learning, so you should have a strong interest and background in professional code development.

Please browse our web page http://biaslab.org for more information on our research goals.

• You should have a Master's degree in electrical engineering, physics, computer science, or similar with excellent grades.
• A record that shows specific interest in signal processing and/or (Bayesian) machine learning
• You will develop open-source code for simulating machine learning experiments, so we put a high value on candidates with a strong professional record and interest in developing code. Please supply some evidence for your programming record, e.g., a link to your Github page.
• In order to complete a PhD, it will be essential to have a good written and spoken command of English. If you have written a MSc thesis (or published a scientific paper) in English, please send it along with your application.
• Finally, we appreciate a team player attitude, willingness to work hard and know how to have fun at it.

• A meaningful job in a dynamic and ambitious university with the possibility to present your work at international conferences.
• A full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months.
• To develop your teaching skills, you will spend 10% of your employment on teaching tasks.
• To support you during your PhD and to prepare you for the rest of your career, you will make a Training and Supervision plan and you will have free access to a personal development program for PhD students (PROOF program ).
• A gross monthly salary and benefits (such as a pension scheme, pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labor Agreement for Dutch Universities.
• Additionally, an annual holiday allowance of 8% of the yearly salary, plus a year-end allowance of 8.3% of the annual salary.
• Should you come from abroad and comply with certain conditions, you can make use of the so-called ‘30% facility’, which permits you not to pay tax on 30% of your salary.
• A broad package of fringe benefits, including an excellent technical infrastructure, moving expenses, and savings schemes.
• Family-friendly initiatives are in place, such as an international spouse program, and excellent on-campus children day care and sports facilities.

More information

Do you recognize yourself in this profile and would you like to know more? Please contact
prof.dr.ir. Bert de Vries, bert.de.vries[at]tue.nl, team web http://biaslab.org.

For information about terms of employment, click here or contact HRServices.flux[at]tue.nl

Please visit www.tue.nl/jobs to find out more about working at TU/e!

Application

We invite you to submit a complete application by using the 'apply now'-button on this page.
The application should include a:

• a cover letter explaining your motivation and suitability for the position;
• a detailed Curriculum Vitae;
• a written scientific report in English (MSc thesis, traineeship report or scientific paper)
• copies of diplomas with course grades (transcripts).

We look forward to your application and will screen it as soon as we have received it.

We do not respond to applications that are sent to us in a different way.

Please keep in mind you can upload only 5 documents up to 2 MB each. If necessary please combine files.