Doctoral fellow - Department of Electronics and Information Systems

Organisation/Company Ghent University Research Field Biological sciences » Biology Biological sciences » Other Computer science » Programming Computer science » Other Engineering » Biomedical engineering Engineering » Electrical engineering Mathematics » Applied mathematics Neurosciences » Other Researcher Profile First Stage Researcher (R1) Final date to receive applications 30 Sep 2026 - 21:59 (UTC) Country Belgium Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme?

Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Department TW06 - Department of Electronics and Information Systems

Degree Master's degree in Computer Science Engineering, Electrical Engineering, Biomedical Engineering, Applied Mathematics, or a related degree

Occupancy rate 100%

Vacancy type Research staff

About IDLab UGent
Ghent University is a world of its own. Employing more than 15.000 people, it is actively involved in education and research, management and administration, as well as technical and social service provision on a daily basis. It is one of the largest, most exciting employers in the area and offers great career opportunities. With its 11 faculties and more than 85 departments offering state-of-the-art study programmes grounded in research in a wide range of academic fields, Ghent University is a logical choice for its staff and students.
Within the faculty of Engineering and Architecture, the IDLab (Internet technology and Data science Lab, UGent-imec) research group performs research on (1) Connectivity and (2) Data Science & Artificial Intelligence. In these research areas we focus on (1) foundations, (2) System Design and (3) Applications. IDLab collaborates with many universities and research centres worldwide and jointly develops advanced technologies with industry (R&D centers from international companies, Flanders' top innovating large companies and SMEs, as well as numerous high-tech start-ups).

In order to support the research, IDLab created a unique research infrastructure used in numerous national and international collaborations.
IDLab is also a core research group of imec, the world-leading research and innovation hub in nanoelectronics and digital technologies. IDLab staff counts about 50 professors, 60 Post Doc researchers, 200 PhD researchers and 40 other staff members. These are spread over about 20 research teams.

Job description
In vitro Multi-Electrode Arrays (MEAs) are a nonperturbative method used in neuroscience to measure the electrical activity of a network of cultured neurons. Such in vitro neuronal cell cultures have been developed primarily to allow the study of disease mechanisms and drug response in a controlled setting. However, they also open up avenues to explore the brain’s mechanisms for information processing and learning, potentially inspiring novel paradigms that advance beyond traditional (silicon) computing.
This PhD position aims to understand and advance in vitro neural models by means of quantitative mechanistic models that capture the biological dynamics of neuronal cultures and enable principled inference from experimental data. Such computational models provide parameters with a clear biological interpretation as data summaries. Additionally, simulations from such models can be used to explore novel in vitro paradigms before implementing them.
More specifically, your main tasks will include:

• Support our ongoing research on mechanistic modelling and Bayesian parameter inference from in vitro neural models.
• Combine theory, simulation, and data-driven methods, this in close interaction with the researchers developing the in vitro cultures and models. You support the design of new experiments through model-based analysis and simulation.
• Discover appropriate mechanistic modelling paradigms to represent the in vitro cultures with enough detail to allow interpretation, always keeping in mind and working towards efficiency of execution of simulations based on these models.
• Performing parameter estimation and uncertainty…