Sven Degroeve leads a team of Machine Learning researchers as part of the VIB Computational Omics and Systems Biology Group (CompOmics). With a focus on modelling the behavior of molecules in state-of-the-art biotechnologies (such as Liquid Chromatography-Mass Spectrometry) using deep neural architectures, the team recently expanded its interests to pre-trained full protein sequence, end-to-end models.

Supported by VIB, the Degroeve team maintains high-end GPU servers that make recent advances in protein AI available for biotechnology researchers within VIB. Such recent advances are AlphaFold2 and AlphaFold-multimer. These single differentiable end-to-end models predict the 3D (folding) structure for a protein sequence or protein complex and a corresponding parameterized multiple sequence alignment with similar proteins.

The Eyckerman lab has an interest in the characterization of human protein complexes in health and disease. The team has developed new experimental methods and has implemented state-of-the-art technologies to study these complexes within several collaborative projects related to a.o. cancer, inflammation, and antiviral research.

We look for a candidate with an analytic background that will be at the interface between the Degroeve and the Eyckerman team. You will follow, apply and further optimize state-of-the-art predictive AI research and build bioinformatics tools to analyze specific datasets and tasks generated by the Eyckerman lab. The main focus will be on the computational identification and validation of experimentally observed protein-protein interactions, how these protein complexes change through the intracellular space and time (protein complex dynamics) as well as how they vary between different cell types.

Profile

• A Master's degree in mathematics, computer sciences, biomedical engineering, computer science engineering, or a Master's degree in bioinformatics, biomedical sciences, bioscience engineering, (bio)chemistry, (molecular) biology, or biotechnology with a keen interest in machine learning, deep learning, or related disciplines.
• Solid programming experience is required (preferably Python)
• Experience with machine-learning/statistical analysis is required
• Experience with machine learning packages such as PyTorch or Tensorflow is a plus
• Bio-informatics experience is a plus
• You are eligible to apply for a grant with our local science foundation (https://www.fwo.be/en/fellowsh... or https://www.fwo.be/en/fellowships-funding/phd-fellowships/phd-fellowship-strategic-basic-research/)
• Enthusiastic, curiosity-driven scientist
• Team player with the ability to take initiative and solve problems
• Highly motivated to understand work on the interface between computer science, biology, and structural biology and willing to learn from wet-lab experiments and from other fields
• Critical, scientific mindset, keen interest in the recent discoveries in biomedical sciences
• Good oral and written communication skills in English

We offer

• A starting contract for one year with a competitive salary to prepare for grant application and defense and to explore alternative funding opportunities.
• A working environment with enthusiastic fellow Ph.D. students, technicians, and postdocs.
• An environment where your bio-informatics findings can be tested in practice.
• Access to state-of-the-art tools and infrastructure that are needed to fulfill your Ph.D. project.
• A stimulating and supportive international research environment.
• Institutional training opportunities to broaden your expertise and skills.

Interested?

For more information, please contact Sven Degroeve ([email protected]) or Sven Eyckerman ([email protected]).

Please apply online and upload your resume and motivation letter:https://jobs.vib.be/apply/4422...

Interested candidates are invited to apply as soon as possible. Applications will be welcome until the position is filled.