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PD in development of an automated assessment and feedback system for education

PD in development of an automated assessment and feedback system for education

Are you motivated to improve education at our university? Do you get inspired by developing artificial intelligence for assessment and feedback? Do you get energy from working with students and teachers? Then this might be the perfect position for you.
(Postdoctoraal) onderzoeker
Irène Curie Fellowship
Electrical Engineering


The high-level learning goal of the Machine Learning for Signal Processing (ML4SP) course is to educate students towards a fundamental understanding of the theoretical background of deep learning methods and demonstrate their similarities to many other, since long-established, signal processing methods. The course focuses specifically on those methods that embed strong priors in the associated neural network architecture to enable data and memory-efficient deep learning, characteristics that often prove vital in healthcare applications.

When designing a deep learning method, a user typically makes many implicit assumptions. These assumptions to a large extent determine the success of the neural network. In the ML4SP course, these assumptions are made explicit, providing the students the means to better design their neural networks and to also enable them to design neural networks that combine physics- and data-driven models. Many neural network designs disregard knowledge that the user already has about underlying structures in the data or about characteristics in the data that are affected by the sensors with which the data is collected. These ignorant neural networks will need to learn all these structures, requiring lots of data. Unfortunately, in many applications
(e.g. healthcare) data is scarce and costly. By embedding knowledge that the user already has, the neural network will only need to learn those aspects that the user cannot describe with physical models yet. This leads to high data efficiency, smaller memory footprint, and faster learning.

The course design is such that theoretical lectures are combined with practical assignments. During these assignments, students will implement various neural networks, such as variational autoencoders, to bring the theory into practice and deepen their knowledge and understanding of the course content. From an educational perspective, these assignments have proven relevant for achieving the learning goals of the course and they are appreciated by the students, as evidenced by their high ratings. Unfortunately, at the same time, these assignments pose a challenge to the design and execution of the course. Ideally, students would get the freedom to experiment with their neural network designs in the way they think is best and subsequently receive detailed feedback about the rights and wrongs of their ideas, closing the learning cycle and making the course design effective from a didactic point of view.

The primary objective of the project is to develop a method for assessing programming codes of students for assignments of the course Machine learning for signal processing to:

  • provide quantitative feedback by means of a grade;
  • provide qualitative feedback by means of detailed feedback to students about mistakes and possible improvements.

As a secondary objective, the method should generalize to other types of assignments to enable its use in other courses as well.

The project is expected to yield a computer-based tool that can be tuned to the specifics of a given assignment and that can import and assess student submissions, providing both quantitative and qualitative feedback. During the project, the performance of this tool will be evaluated by applying it to a database of submissions and their assessment that has been collected over the years of the course.

This project will be embedded within the Signal Processing Systems group at the Department of Electrical Engineering, Eindhoven University of Technology and funding has been obtained from the BOOST! program that aims to innovate teaching methods.


  • A PhD degree in Computer Science or Electrical Engineering with strong affinity in AI design and experience with NLP.
  • A passion for education.
  • Ability to work in a team and participate in the preparation and execution of the course, enabling live experimentation with some of the project results.
  • Capability to coordinate your own research and interact with various disciplines and levels of expertise.
  • Fluent in spoken and written English.


A Postdoc position in a vibrant and dynamic high-tech environment. You can perform research on a highly relevant topic that can influence the quality of our education and help students, with the potential to see your research results get implemented in the ML4SP course, but ideally also other courses. Furthermore, we offer

  • A full-time employment for 1.5 years.
  • A gross monthly salary starting from € 3.557,- (on a fulltime basis), depending on experience and knowledge. The salary is in accordance with the Collective Labor Agreement of the Dutch Universities.
  • A year-end bonus of 8.3% and annual vacation pay of 8%.
  • High-quality training programs on general skills, didactics and topics related to research and valorization.
  • An excellent technical infrastructure, on-campus children's day care and sports facilities.
  • Partially paid parental leave and an allowance for commuting, working from home and internet costs.
  • A TU/e Postdoc Association that helps you to build a stronger and broader academic and personal network, and offers tailored support, training and workshops.
  • A Staff Immigration Team is available for international candidates, as are a tax compensation scheme (the 30% facility) and a compensation for moving expenses.

Informatie en sollicitatie

About us

Eindhoven University of Technology is an internationally top-ranking university in the Netherlands that combines scientific curiosity with a hands-on attitude. Our spirit of collaboration translates into an open culture and a top-five position in collaborating with advanced industries. Fundamental knowledge enables us to design solutions for the highly complex problems of today and tomorrow. 

More information

Do you recognize yourself in this profile and would you like to know more? Please contact R. Vullings, Associate Professor, r.vullings[at]

Visit our website for more information about the application process or the conditions of employment. You can also contact the HR department: hrservices.flux[at]

Are you inspired and would like to know more about working at TU/e? Please visit our career page.


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

  • Cover letter in which you describe your motivation and qualifications for the position.
  • Curriculum vitae, including a list of your publications and the contact information of three references.
  • Brief description of your PhD thesis.

We look forward to your application and will screen it as soon as we have received it. Screening will continue until the position has been filled.

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.