Description
We are seeking a motivated new PhD candidate who wants to join an exciting collaborative research program within the VIB-Center for Inflammation Research between the Guilliams, Saelens and Saeys teams.
In this research project you will develop and apply algorithms to link clinical phenotypes of metastasis to molecular phenotypes in mouse models. It is known that metastases exhibit several types of spatial growth patterns which correlate with therapy efficiencies and overall survival (Vermeulen et al. J Pathol. 2001, Nielsen et al. Mod Pathol. 2014, Baldin et al. J Pathol Clinical Research 2021, Frentzas et al. Nature Medicine 2016). What is exactly driving these growth patterns is unknown, although the involvement of the tumor microenvironment is clear. These patterns can be mimicked with in vivo models of metastasis, which provides unique opportunities to mechanistically dissect what drives the different cell states. You will link clinically relevant phenotypes to putative murine microenvironments and use high-resolution spatial omics techniques to prioritize molecular factors that may drive the immune cell states. These will then be validated through in vivo screening, first on whether these indeed drive these aberrant states, and second on whether these indeed drive the growth pattern.
You will be embedded both within an experimental and computational team, providing a unique atmosphere where there is expertise to dig deep into computational modelling, while remaining connected to the experimental side. This interdisciplinary atmosphere has been a main catalyst for many past successes: https://europepmc.org/article/MED/35021063, https://europepmc.org/article/MED/31819264, https://europepmc.org/article/MED/31561945, https://europepmc.org/article/MED/39747019, https://europepmc.org/article/PPR/PPR800886.
Profile
- Master's in bioinformatics, biomedicine, bioengineering, biotechnology or related fields
- Interest in linking digital pathology with mechanistic experimental biology
- Programming experience in Python
- Excellent communication skills and fluency in English
- Collaborative personality with attention for detail
- Experience in imaging or spatial omics data analysis
- Background in biomedicine and digital pathology
- Embedding within a computational team, with extensive experience in computational biology and machine learning.
- Embedding within an experimental team, with direct availability of experimental validation for machine learning models.
- Competitive salary and full benefits.
- Access to state-of-the-art computing infrastructure.
- Fully funded for 4 years, although the candidate will be encouraged to apply for personal funding.
- Motivation letter of 1-1.5 pages
- Curriculum vitae
- University degree certificates
Want to apply?
Sent your application through the online tool at
For more information, you are welcome to directly contact Prof. Wouter Saelens (wouter.saelens@ugent.be), Prof. Yvan Saeys (yvan.saeys@ugent.be) or Prof. Martin Guilliams (martin.guilliams@ugent.be)
Diversity & Inclusion
We are committed to creating and sustaining an inclusive, respectful, and collaborative environment where everyone can thrive. We value diversity in all its forms - including but not limited to gender identity, ethnicity, nationality, disability, sexual orientation, age, socio-economic background, and family situation. We welcome applications from individuals of all backgrounds and identities, and we are dedicated to providing equal opportunities and actively promoting a culture of belonging.
Feel free to let us know in your cover letter if there are any past or current circumstances that can impact your application.
By embracing the unique perspectives and experiences of our team members, we aim to foster innovation and advance excellence in research. We believe that a diverse and inclusive workplace is essential for scientific creativity, effective collaboration, and impactful discovery.