Undergraduates & Masters
The Centre for Genomic Regulation (CRG) aims to provide highly motivated undergraduate and master students the opportunity to conduct research at the CRG. The goal is to encourage students (from all nationalities) in the pursuit of a scientific career giving them the prospect to get experience in an international laboratory while improving their skills.
The CRG is a center of excellence with international teams representing a broad range of disciplines, with first class core technologies to support the research projects, a wide range of seminars given by high-profile invited speakers, and courses on complementary and transferable skills integrated with the training programme.
We accept applications throughout the year for any type of internship with a learning agreement with your university. Have a look at our labs and research programmes and contact the Group Leader of your choice directly with the following documents attached:
- Motivation letter
- CV
- Reference letter
- University transcripts
Acceptance will depend on the capacity of the research group and the ongoing projects.
We host online events and workshops to inform you about various opportunities available at the CRG and guide you on how to search for PhD positions. If you are keen to learn more, please check HERE.
Fellowships available for the academic year 2026/2027:
We are pleased to offer 10 fellowship opportunities across different research projects at the CRG. Each project will remain open until the right candidate is found, giving motivated students the chance to join our vibrant research community and contribute to cutting-edge science.
Please remember that the candidate must be enrolled in an official undergraduate or MSc programme at a university, as an agreement will have to be signed between the candidate’s University and the CRG.
Publication date of these fellowships: August 6th, 2026
Fellowships conditions
- Stipend: 700€/month/gross – up to 4 months
- Travel support: Return ticket (up to 1000€/non European flights / up to 400€ for European flights)
- Eligibility: The fellowship can only be given to new recruits, not students already at the CRG
- Timing: The fellowship needs to be given within the 2026/2027 academic year
Application Procedure:
Read carefully the projects below and, if interested, contact the project supervisor and group leader. Please include the following documents in your application:
- Motivation letter
- Curriculum Vitae (CV)
- Reference letter
- University transcripts
Probabilistic machine learning and genomics - Dias & Frazer Lab
PROJECT DESCRIPTION - Folding versus function: disentangling the molecular mechanisms of missense variants in gene-trait associations
Protein language models | Missense variants | Protein stability | Protein folding | Human genetics | UK Biobank | Variant effect prediction | Computational biology | Machine learning | Protein biophysics
Genome sequencing has grown exponentially, but determining the phenotypic consequences of missense variants remains a key challenge in human genetics. Deep-learning unsupervised methods, such as protein language models (pLM), that learn the distribution of sequence variation across organisms have emerged as promising tools for scoring variant effects. In gene-trait association studies, regression tests using pLMs find ∼50% more associations than burden tests (Jang et al., Cell Genomics 2026) and can be run directly from precomputed summary statistics (Dinh et al., Nature Methods 2026). However, pLM are trained on evolutionary data and therefore often fall short of attributing effects to specific biophysical mechanisms.We propose to integrate protein language models with folding energetics to disentangle effects on stability and on functions beyond folding, such as allostery, ligand/partner binding and downstream interactions. We will quantify and separate mutational effects on the energetics of protein folding versus natural selection by computing protein “dark energy”, defined as the difference between physical folding free energies and evolutionary free energies derived from a pLM (Galpern et al., PNAS 2026). We will apply this biophysical decomposition to study the genotype-phenotype summary statistics from the UK Biobank as provided by Genebass (Karczewski et al., Cell Genomics 2022), focusing specifically on genes associated with blood biochemistry biomarkers. We will relate the predicted folding-stability and dark energy changes for each missense variant to the effect sizes and contrast those regressions against the gene's putative loss-of-function (pLoF) effect. We will disentangle variants destabilizing the encoded protein from others that preserve protein stability but affect specific chemical activities. Together, this work will pave the way to a scalable framework to move beyond statistical gene–trait associations toward a residue-level, mechanistic understanding of how missense variation perturbs protein biology in human genetics.
WHO ARE WE LOOKING FOR?
We are looking for a motivated master student with a background in bioinformatics, computational biology, computer science, physics, mathematics, or a related quantitative discipline. Previous programming experience in Python is expected. Familiarity with molecular biology, genetics, statistics, or machine learning is desirable but not required. The student should be curious and interested in applying computational methods to biological questions.
HOW TO APPLY
Send applications documents to the PI and Ezequiel Galpern - HERE
Contact
For any further questions, please contact
CRG Training & Academic Office
Centre de Regulació Genòmica
Dr. Aiguader, 88
PRBB Building
08003 Barcelona
training@crg.eu