PhD in Computational Modelling of Bone Regeneration in Compromised Environments

Welcome to Maastricht University!
This vacancy represents an exciting opportunity for ambitious individuals to join the computational group led by Aurélie Carlier as part of the DRIVE-RM program. The PhD researcher will perform cutting‑edge research in computational modeling methods applied to regenerative medicine and more specifically, to bone regeneration.

• Our goal: Advance regenerative medicine by developing in silico models that reveal how bone regenerates including how this process changes in compromised biological environments.
• Your colleagues: A collaborative team of computational modelers, biologists, and engineers at MERLN, working at the interface of data-driven and mechanistic modelling.

Regenerative medicine (RM) holds the promise to cure many of what are now chronic patients, restoring health rather than protracting decline, bettering the lives of millions and at the same time preventing lifelong, expensive care processes: cure instead of care. The scientific community has made large steps in this direction over the past decade, however, our understanding of the fundamentals of cell, tissue and organ regeneration and of how to stimulate and guide this with intelligent biomaterials in the human body is still in its infancy.

Specifically, it is crucial to understand the underlying regenerative mechanisms and how these are altered in compromised environments (e.g. due to aging or co‑morbidities such as diabetes or chronic kidney disease). This research project aims to develop and use in silico models to simulate the bone regeneration process, to improve our fundamental understanding thereof, and use the obtained knowledge to design improved regenerative medicine strategies.

• Development of a bone regeneration map
• Computational modelling of bone regeneration (partial differential equations)
• Bringing novel, cutting‑edge approaches (e.g. PINNs) to modeling of regenerative processes
• Parameter optimization and sensitivity analysis
• Analysis and integration of various in vitro/in vivo data for model calibration
• Approximately 5% of your time will be dedicated to tutoring, teaching, or supervising students.

Are you ready to push boundaries in regenerative medicine? Then we would love to meet you.

• Computationally skilled and analytically strong – You have a Master’s degree in mathematical biology, computational mathematics, biomedical engineering, numerical optimization, biophysics, systems biology, bioinformatics, computer science, or a related field.
• Experienced programmer – You are confident in languages such as Python, Matlab or R, and you enjoy building and improving computational pipelines.
• Proficient in numerical methods – You can translate biological processes into mathematical or computational frameworks.
• Curious about innovative techniques – You are eager to explore cutting‑edge approaches, such as physics‑informed neural networks (PINNs), and apply them to regenerative processes.
• Collaborative by nature – You enjoy working across disciplines and feel at ease in an international, interdisciplinary environment.
• Communicative and clear – You can express your ideas effectively in English, both in writing and verbally;
  Dutch is not required.
• Independent and proactive – You take initiative, work reliably, and contribute to a constructive team atmosphere.

Additional training opportunities will be provided based on your skills.

At Maastricht University, you’ll work in an international, open, and engaged environment. We offer:

• A 12‑month contract with the prospect of a 3‑year extension, based on positive evaluation.
• A gross monthly salary between €3059,00 and €3881,00 (based on full‑time employment of 38 hours per week; scale P), plus 8% holiday allowance and an 8.3% year‑end bonus.
• 29 vacation days (based on full‑time employment), four additional public holidays.
• Flexible working hours, a home office allowance, and the option to work from home.
• Freedom and space to shape your research independently and develop your ideas.
• A close‑knit interdisciplinary community to collaborate and grow…