PhD position AI-driven functional DNA interpretation for molecular diagnostics

In this role, you will contribute to cutting-edge computational methods that can help provide answers to millions of undiagnosed patients. Your work will focus on revolutionizing the classification of genetic variants and diseases using functional evidence and explainable AI, bridging the gap between advanced machine learning and clinical application.

Your tasks are:
- Exploring molecular dynamics and proteomics methods to enhance variant interpretation, which may also include a role for non-coding variation (e.g. promotors, enhancers, TFBSs, TADs, lncRNAs, etc)
- Developing AI models for classifying genetic variants based on functional impact that are explainable and clinically interpretable.
- Translating cutting-edge computational science into medical practice, in particular maximizing use of all phenotypic and molecular data collected.
- Collaborating with bioinformaticians, lab specialists and clinicians to integrate findings into real-world diagnostics.

Project AI-driven functional DNA interpretation for molecular diagnostics
Rare genetic diseases affect approximately 1 in 10 people, yet the majority never receive a molecular diagnosis. Without a diagnosis, patients are left without a prognosis, effective treatment options, or access to the right support groups. A molecular diagnosis hinges on classifying DNA variants as pathogenic (i.e. disease-causing) or benign and assignment to disease phenotypes. A significant portion of these variants—single base-pair changes known as missense variants—alter protein structures and functions. Despite decades of research and hundreds of predictive tools, about half of these missense variants are classified as Variants of Unknown Significance (VUS), meaning there is not enough evidence for a classification as either benign or pathogenic. As a result, much DNA variation with remains untapped, preventing life-changing diagnoses for countless individuals.

The field is currently undergoing a paradigm shift, moving beyond traditional prediction models based on indirect evidence (such as evolutionary conservation and allele frequencies) toward direct functional evidence, including protein stability and activity. This shift is driven by recent breakthroughs such as AlphaFold and AlphaMissense, opening new avenues for variant interpretation. In addition, better phenotypes can be extracted from health records using AI.

In this PhD project, you will work on developing computational methods that generate close approximations of functional evidence and meaningful predictions for variant classification, with a strong focus on explainability. This means not only building AI models but also carefully selecting and interpreting functional features to ensure clinical relevance. Once developed and validated, your methods will be implemented in a clinical setting, directly impacting patient care.

- You are a highly motivated, creative, and technically skilled researcher with an MSc degree in Bioinformatics, Computational Biology, Artificial Intelligence, Data Science, Biomedical Engineering, Molecular Biology, or a related field.
- You have an interest in, or experience with, molecular dynamics, protein structure analysis, and AI-driven biomedical applications.
- Strong programming skills (Java, R or Python preferred) and experience with machine learning are a plus.
- You have a talent for communicating results and building bridges between science and practice. This entails explaining models and predictions to lab specialists and clinicians.

- A dynamic research environment at the forefront of AI-driven healthcare innovation.
- Access to diverse, real-world medical data sets and cutting-edge computational resources.
- Support and collaboration with MOLGENIS large open source scientific software team to help you deploy and test your methods in working solutions.
- Mentorship by leading experts in AI, genomics, and clinical informatics.
- Opportunities to publish in high-impact journals and present at international conferences.
This is a full-time PhD contract for 4 years and an excellent environment for further development. First, a temporary one-year position will be offered with the option of renewal for another 3 years. Your salary will be a minimum of € 3.017,- gross per month in the first year and a maximum of € 3.824,- (PhD salary scale) in the final (4th) year, based on a full-time appointment. In addition, the UMCG will offer you 8% holiday pay, and 8.3% end-of-year bonus. The conditions of employment comply with the Collective Labour Agreement.

If you’re excited about pushing the boundaries of AI in precision medicine and making a tangible difference in rare disease diagnostics, we encourage you to apply!

The position is part of the Genomics Coordination Centre (GCC), the ‘big data science’ research & service hub of the University Medical Centre Groningen (UMCG) and University of Groningen (rank 66 worldwide, 3rd best place to work in EU), hosted by the Department of Genetics. Our mission is to accelerate scientific discovery in health data with innovative methods and tools that expedite medical research and improve people's lives, using open source software and large computer ‘clouds’, in particular the MOLGENIS software that we lead, but also DataSHIELD, Singularity, RedCap, XNAT, OpenStack etc.

Joeri van der Velde
06 1981 4646
k.j.van.der.velde@umcg.nl

Joeri van der Velde, k.j.van.der.velde@umcg.nl, telefoonnummer: 06 1981 4646