Assistant Professor in structured data science

Research / Academic

Retrieving useful information out of large data sets is receiving an increasing amount of attention these days. Emerging applications like autonomous driving, the square kilometre array for radio astronomy, biomedical sensing systems, the internet-of-things with millions of users and sensors, all generate enormous amounts of data and it is not always clear how to gather, process, and analyze that data in an efficient and rigorous manner. Data science provides a solution to this problem. It basically yields a set of tools for data mining, data cleansing, machine learning and data analysis.

While these tools can tackle a wide variety of problems, computer science approaches often ignore the structure that is present in the problem due to the physics. This structure could come from the prior knowledge of the model that generates the data (e.g., radio frequency channel models, biomedical signal models, diffusion models), or it could directly relate to the structure in the data (e.g., space-time, sparsity, network/graph data). Taking such structure into account will aid many of the existing data science tools, making them easier to interpret and simpler to implement. This field of structured data science is shaped by the nontrivial interplay between conventional signal processing and conventional data science. The goal is to illuminate and explore this interplay and apply it to the earlier mentioned applications.


The opening for an Assistant Professor is intended to further develop this area. A background in statistical signal processing/modelling and the ability to apply this to data science/machine learning is required.

The candidate will be placed in the CAS group of EEMCS. This world-renowned group covers the theory and applications of signal processing, including high-level digital system design. Generally we search for candidates with a strong signal processing background complementary to the expertise that is already present in the group. Experience with biomedical signal and image processing applications is of interest.

The candidate will also be involved in teaching and e.g. develop new courses on structured data science and machine learning for Electrical Engineering students.

While this position is defined as a tenure-track Assistant Professor position, excellently qualified but more senior researchers are also invited to apply. For this position, preference will be given to female applicants.

Candidates should have (1) a PhD degree in Electrical Engineering or a closely related discipline, with outstanding academic credentials, (2) several years of working experience as a Postdoctoral Researcher in an academic institution, and (3) the ambition to be a future scientific leader in the mentioned area.

Salary Benefits:

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities. The TU Delft offers a customisable compensation package, a discount on health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged.

For international applicants we offer the Coming to Delft Service and Partner Career Advice to assist you with your relocation. An International Children's Centre offers childcare and there is an international primary school.
Inspiring, excellent education is our central aim. We expect you to obtain a University Teaching Qualification (UTQ) within three years if you have less than five years of teaching experience. This is provided by the TU Delft UTQ programme.

TU Delft sets high standards for the English competency of the teaching staff. The TU Delft offers training to improve English competency. If you do not speak Dutch, we offer courses to learn the Dutch language.

Work Hours:

38 - 40 hours per week


Mekelweg 2