PROFESSOR IN MATHEMATICAL MODELLING OF COMPLEX BIOMEDICAL SYSTEMS
The closing date for this job submission has passed.
KU Leuven has a full-time academic vacancy in the area of biomedical data processing in ESAT-STADIUS research group at the Leuven campus. ESAT-STADIUS (STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics) (www.esat.kuleuven.be/stadius/) is a Research Unit in the Department of Electrical Engineering/ESAT of KU Leuven, joining the research and educational activities of 12 professors and over 80 PhD and postdoctoral researchers. We are looking for internationally oriented candidates with an excellent research record and with educational competence. The successful candidate will be appointed in the Department of Electrical Engineering and the Faculty of Engineering Sciences of the Science, Engineering and Technology Group of KU Leuven.
You establish an internationally relevant research program in the area of mathematical modelling of complex biomedical systems, both theoretical and application-oriented, and based on data-driven and/or physiologically inspired models. This research program can take several directions (non-exhaustive, non-restrictive list):
-advanced modelling of complex and non-linear biomedical systems and processes, both in healthy and pathological conditions. This includes -amongst others- the functioning of physiological processes and their mutual interactions in physiological networks, taking account of their dynamics, such as during development and growth processes, and the progression of diseases.
- models integrating different levels at different scales ('multilevel'), e.g., from cellular to organ level, from omics-info (genomics, proteomics, metabolomics) to corresponding biomedical signals and images, etc.
-processing and interpretation of multimodal measurements, such as the combination of EEG, fMRI, MEG, and ECoG for brain research, or the combination of audio, accelerometry, ECG, EMG, and EEG for epilepsy research.
-applications in a clinical context, with focus on automation, robustness (with regard to missing data, artifacts, uncertainty), and improved diagnostic intelligence for decision support.