PhD - Leuven | Just now
Microphysiological systems (MPS) are emerging as transformative tools in biomedical and clinical research, enabling the study of complex, dynamic interactions between human tissues and cells in controlled, biomimetic environments. This PhD project integrates nanotechnology, microfluidics, biomedical sciences, and regenerative medicine to address one of the central open questions in degenerative disease: how the central nervous system (CNS) and peripheral nervous system (PNS) regulate local tissue homeostasis and drive pathological remodeling.
Degenerative diseases caused by fibrosis are increasingly recognized as disorders of dysregulated neuro-immune-tissue communication. Aberrant signaling between the CNS, PNS, and local tissues promotes maladaptive repair, inflammation, and fibrosis, leading to progressive loss of function. Despite recent advances in neurobiology and musculoskeletal research, the mechanisms initiating and sustaining this process remain poorly defined, largely due to the lack of physiologically relevant experimental models.
This project will develop a compartmentalized in vitro microfluidic MPS that integrates CNS, PNS, and human joint tissue components within a dynamic, multi-cellular platform. Leveraging imec’s nanotechnology innovations, the system will enable real-time, high-resolution analysis of intercellular communication across neural and musculoskeletal compartments. By recreating disease-relevant microenvironments, the model will provide mechanistic insights into the nervous system’s contribution to joint degeneration and fibrosis.
The outcomes of this research will establish a novel class of biomimetic models for neuro-immune-tissue crosstalk, provide predictive data for therapeutic development, and foster cross-disciplinary collaborations. Ultimately, this work aims to redefine how we study, understand, and treat degenerative diseases at the systems level.
Required background: Nanotechnology, biomedical sciences, biomedical engineering
Type of work: 20% development, 30% modeling/simulation, 50% experimental
Supervisor: Liesbet Lagae
Co-supervisor: Johanna Bolander
Daily advisor: Neha Deshpande
The reference code for this position is 2026-098. Mention this reference code on your application form.