Martin Guilliams

Join the VIB-Center for Inflammation Research as a PhD candidate in an interdisciplinary project at the intersection of computational and experimental biology. This collaborative research program, led by Prof. Martin Guilliams, Prof. Wouter Saelens, and Prof. Yvan Saeys, aims to unravel the molecular mechanisms driving spatial growth patterns of metastasis using advanced mouse models. The project focuses on developing and applying algorithms to link clinical phenotypes of metastasis with molecular phenotypes, leveraging high-resolution spatial omics techniques to identify and validate key molecular drivers within the tumor microenvironment. You will work in a dynamic environment, embedded within both computational and experimental teams, providing access to expertise in computational modeling, machine learning, and direct experimental validation. The research is highly interdisciplinary, offering opportunities to bridge digital pathology with mechanistic experimental biology. The teams have a strong track record of impactful research, as evidenced by multiple high-profile publications. The position is fully funded for four years, offering a competitive salary, full benefits, and access to state-of-the-art computing infrastructure. While funding is secured, candidates are encouraged to apply for personal fellowships. The VIB-Center is committed to diversity, inclusion, and equal opportunity, fostering a collaborative and innovative research environment. Applicants should have a Master's degree in bioinformatics, biomedicine, bioengineering, biotechnology, or related fields, with programming experience in Python and strong communication skills in English. Experience in imaging or spatial omics data analysis and a background in digital pathology are advantageous but not required. To apply, submit your application online via the VIB application tool, including a motivation letter, CV, and degree certificates. For further information, you may contact the supervisors directly by email.

This PhD position offers an exciting opportunity to join a collaborative research program at the VIB-Center for Inflammation Research, working with the Guilliams and Saelens teams. The project focuses on developing advanced probabilistic deep-learning models to automatically extract biological and statistical knowledge from in vivo perturbational omics data. The research leverages cutting-edge single-cell CRISPR technologies, enabling high-throughput screening of molecular factors in vivo during development and disease. These technologies significantly enhance the speed, scale, and resolution of immune cell pathway analysis, providing a rich dataset for training causal machine learning models and advancing precision medicine. The core challenge addressed in this project is the multidimensional nature of omics data, which includes variables such as time, perturbation, tissue position, cell state, and statistical power. By integrating modern probabilistic modeling techniques, including diffusion and transformer models, the team aims to automate data analysis, opening new avenues for data interpretation and predictive modeling. The ultimate goal is to develop active-learning-based screening methods to comprehensively unravel developmental and disease states. As a PhD candidate, you will be embedded in both experimental and computational teams, benefiting from a unique interdisciplinary environment. This setting provides expertise in deep-learning model development and direct opportunities for biological validation. The collaborative atmosphere has driven many past successes, as evidenced by several high-impact publications. You will have access to state-of-the-art computing infrastructure and be encouraged to participate in experimental work to better understand data generation processes. Eligibility requirements include a Master's degree in software engineering, computer science, data science, bioengineering, bioinformatics, engineering, physics, or a related field. Candidates should have experience in machine learning or computational biology, programming proficiency in Python, excellent communication skills, fluency in English, and a collaborative mindset. Additional experience with Pytorch or JAX deep learning models and single-cell or spatial omics data analysis is a plus. The position offers a competitive salary, full benefits, and a supportive, inclusive research environment committed to diversity and equal opportunity. Applications are accepted online via the VIB application tool, with required documents including a motivation letter, CV, and degree certificates. For further information, prospective applicants may contact Prof. Wouter Saelens or Prof. Martin Guilliams. Application deadline: December 31, 2026.