Undergraduates & Masters

PROJECT DESCRIPTION - Uncovering the variations in mutational signature exposures across the human cancer genome

Cancer | Mutations | Mutagenesis | Mutational Signatures | Multivariate Analysistion

Cancer arises due to the accumulation of a critical number of driver mutations that confer cells with aberrant survival advantages. Mutagenesis is, however, a random phenomenon that results from the superposition of distinct mutagenic processes. Different processes leave different footprints in the cancer genome in terms of the type of mutations that they cause. Mutational signature analysis has proved to be useful for deconvolving exogenous and endogenous processes causing specific patterns of mutations across many different cancer types. Classic analysis focuses on the pool of mutations observed everywhere in the genome. However, the action of different mutagenic processes might be modulated differently depending on the specific genomic region and its properties. Previous studies have considered the impact of some epigenetic factors on mutational processes. Here, we aim to expand on this to more precisely probe the drivers of variations in signature intensity. This will provide a more comprehensive view of the modulators of mutational signatures in human cancer.

WHO ARE WE LOOKING FOR?

We are seeking a motivated student with a strong background in quantitative sciences such as computer science, mathematics, physics, engineering, or statistics. Candidates from life sciences (biology, medicine, etc.) are also encouraged to apply, provided they have proven experience in computational or bioinformatics projects. The ideal profile combines expertise from both areas, bridging quantitative and life sciences.

HOW TO APPLY

Please send all required documents mentioned above to the PI and Miguel Ángel Cortes - HERE

Mechanics of Organelle Remodeling - AL JORD Lab

PROJECT DESCRIPTION

Cytoskeletal Forces | RNA Processing | Biomimetic Reconstitution | Mechanobiology

Mechanobiology has shown how external forces shape cell behaviour, but the influence of intracellular forces—especially those generated by the cytoskeleton—remains largely unexplored. This project investigates the idea that cytoskeletal forces can directly impact the nucleus by mechanically stirring the nucleoplasm and thereby enhancing RNA processing.

The student will help develop an innovative in vitro platform that reconstitutes active cytoskeletal networks in cell-sized microwells. By combining isolated nuclei or synthetic nucleus-like vesicles with fluorescent RNA reporters, this system will allow real-time tracking of RNA processing under controlled mechanical inputs. The goal is to uncover how intracellular mechanics regulate gene expression, opening new perspectives on genome regulation and shedding light on disease mechanisms, particularly in cancer where both the cytoskeleton and RNA metabolism are frequently altered.

WHO ARE WE LOOKING FOR?

The ideal candidate should have a strong interest in image analysis and coding, with some experience in Python, or equivalent tools. A background in cell or molecular biology and familiarity with wet lab techniques (e.g., microscopy, RNA handling) would be a strong plus, but not strictly required. Most importantly, the student should be curious, motivated, and open to working in an interdisciplinary environment at the interface of mechanobiology and RNA processing.

HOW TO APPLY

Please send all required documents mentioned above to the PI and Pierre Bercier - HERE

Genetic Systems - LEHNER Lab

PROJECT DESCRIPTION

Protein | Evolution |  Design | Engineering | Biophysics | Energetics

A small 60-amino-acid protein has ~10^78 possible sequences, more than the estimated number of atoms in the Universe. How can we explore and model this vast sequence space efficiently? Can doing so help us understand how evolution found so many stable, functional sequences? And how can it inform the design of new protein therapeutics?

These questions were partially addressed in our recent Science publication, co-authored by a former master’s student in the lab. In this project, we will build on that work by fully randomizing the sequence of a small protein domain and an interacting protein-protein pair. We will obtain experimental measurements of the stability and binding affinity of millions of sequence variants.

Using neural networks, we will fit interpretable thermodynamic models to this data and test whether these models can recover patterns observed in homologous protein sequences across billions of years of evolution. This will allow us to generate the first comprehensive energetic maps of a single protein and a protein-protein interface.

We will investigate to what extent the effects of mutations are interdependent, and use this understanding to reprogram our proteins—e.g., to maximize surface charge, alter core volume, or explore sequences that diverge radically from nature. We will experimentally characterize the stability and function of these designs, laying the groundwork for a new strategy to evolve and engineer proteins, with direct relevance to biomedical challenges such as reducing immunogenicity in protein therapeutics.

WHO ARE WE LOOKING FOR?

We are seeking a motivated student with a genuine fascination for molecular biology—and a special interest in protein science. This project sits at the crossroads of protein biophysics, thermodynamics, and structural biology, while also drawing on genomics and computational methods to generate and analyze large datasets.

Students from backgrounds such as biotechnology, biochemistry, chemistry, or related fields who are eager to develop a hybrid experimental–computational profile will be especially well-suited. Previous experience is not essential, but familiarity with molecular cloning techniques and/or programming in Python or R will be considered an advantage.

HOW TO APPLY

Please send all required documents mentioned above to the PI and Albert Escobedo Pascual - HERE

Comparative Bioinformatics - NOTREDAME Lab

PROJECT DESCRIPTION:

Non-coding DNA | Gene Expression | Sequence Alignment | Deep Learning | Functional and Comparative Genomics

The alignment of non-coding DNA remains a major challenge in comparative genomics. Sequence identity derived from pairwise alignment is often insufficient to determine whether two regions are functionally or evolutionarily related. In the case of protein coding sequences, a common strategy is to translate DNA into amino acid or even structure-based sequences (e.g., Foldseek), which are significantly more conserved and therefore more informative for detecting distant homology relationships.

Similarly, expression patterns tend to be more conserved than the underlying DNA sequence as they are more directly related to function. By integrating experimental gene expression data (e.g., RNA-seq across tissues and species) with deep learning approaches, this project aims to develop an alternative representation of non-coding DNA that better captures evolutionary relationships, thereby expanding the scope of sequence alignment methods beyond the traditional focus on coding and protein-coding regions.
Additionally, the project addresses a broader, open biological question: how to best quantify expression similarity. This is a critical issue in current genomics research and will gain relevance with the growing availability of large-scale, multi-species transcriptomic datasets from global initiatives such as the Earth BioGenome Project.

WHO ARE WE LOOKING FOR?

The student should have a background (or at least a strong interest) in maths/statistics and molecular/evolutionary biology. Basic computational skills are also necessary. Theoretical or hands-on experience with deep learning and common bioinformatic tools or databases would be helpful. English level should be good enough to follow and contribute to technical conversations, as it is the main language in the lab, but Catalan and/or Spanish is also preferred. Besides the technical skills, the student should be curious and critical, as well as enjoy discussing ideas and working together.

HOW TO APPLY

Please send all required documents mentioned above to the PI and Cristina Araiz - HERE

Single Cell Epigenomics and Cancer Development - BEEKMAN Lab

PROJECT DESCRIPTION - The power of DNA hydroxymethylation to refine inference of differentiation trajectories in B-cells 

DNA Methylation | Hydroxymethylation | Single-Molecule Long-Read Sequencing | B-cell Differentiation | Epigenetic Lineage Tracing

This project explores how DNA methylation dynamics, particularly the role of 5-hydroxymethylcytosine (5hmC), can be leveraged for lineage tracing in B-cell differentiation. While standard methods cannot distinguish between 5-methylcytosine (5mC) and 5hmC, long-read nanopore sequencing enables single-molecule resolution of both marks, offering unprecedented insights into active demethylation processes.
The first aim is to establish genome-wide patterns of 5mC and 5hmC across five stages of B-cell differentiation and to examine their relationship with enhancer activation. The second aim is to test whether incorporating hydroxymethylation as a third state improves the accuracy of computational lineage tracing compared to traditional two-state methylation models.

Overall, this work will provide new frameworks to study methylation dynamics and enhance our ability to trace cellular differentiation and cancer evolution.

WHO ARE WE LOOKING FOR?

To pursue this project, the student requires a strong background in programming in either R or python as well as a solid foundation of bash scripting. Knowledge of clustering algorithms and phylogenic inference is appreciated. Furthermore, the student should have a firm grasp of high-throughput sequencing techniques and an understanding of molecular biology, preferably including epigenetics.

HOW TO APPLY

Please send all required documents mentioned above to the PI and Johanna Denkena - HERE

Single cell genomics and evolution - SEBÉ-PEDRÓS Lab

PROJECT DESCRIPTION

Chromatin | Evolution | Transcription | Transposons | Proteomics | Phylogenetics | Biochemistry

In the Sebé-Pedrós lab, we study how chromatin regulation has evolved across the eukaryotic tree of life. Building on our recent work characterizing the diversity of histone post-translational modifications using phylogenomics, proteomics, and an innovative ChIP-seq method, we now aim to uncover the functional crosstalk between chromatin “readers” and “writers.”
This project will combine molecular phylogenetics, proteomics, chromatin profiling, and biochemical approaches. The master student will play an active role in generating new datasets and, importantly, in the computational integration of epigenomic data. The ultimate goal is to advance our understanding of how eukaryotic chromatin states and components have diversified and evolved.

WHO ARE WE LOOKING FOR?

We are looking for a candidate with experience in at least one of the following fields: phylogenetics, proteomics, genomics, or iochemistry. The ideal candidate is pursuing a Master’s in Biology, Biochemistry, or related disciplines, with a Bachelor's degree in Biology, Biomedicine, or a related field. They should have basic molecular biology skills, some knowledge of chromatin and evolution, and programming skills in R or other scripting languages (Python/Perl). Additionally, the candidate should demonstrate a strong desire to expand their scientific knowledge and skillset and possess a collaborative and friendly attitude. Optional but desirable skills include experience in omics data generation, sterile culturing, and utilising HPC clusters. 

HOW TO APPLY

Please send all required documents mentioned above to the PI and Sean Montgomery - HERE

Oocyte Biology & Cellular Dormancy - BÖKE Lab 

PROJECT DESCRIPTION - Long live the oocyte: studying proteostasis in oocytes to understand their longevity

Female Infertility | Ageing | Oocytes | Proetostasis | In Vitro Ovary Culture

Oocytes form during embryonic development and persist in a dormant state for decades, representing some of the longest-lived cells in our body. As oocytes transfer their cytoplasm and genetic material to the early embryo and the next generation, the regulation of their quality serves as a unique evolutionary strategy to ensure species survival and fertility. Protein turnover is essential for replacing damaged molecules with their functional copies since long lifespans expose proteins to damage accumulation, including age-related insults. Recent data from our and other laboratories show that oocytes have low protein turnover rates, implying that they have unique yet unknown strategies to preserve a healthy cytoplasm and to ensure long-term organism fertility. With this innovative project, we aim to dynamically map proteins that are produced during oocyte formation and persist throughout their lifetime, characterize the molecular mechanisms by which they are regulated, and develop new culture conditions to preserve fertility in vitro. We are combining state-of-the-art mass spectrometry experiments with high-resolution fluorescent imaging to characterize and quantify oocytes’ dynamic proteome and study protein lifespan regulation. Based on these results, we are developing new in vitro protocols to culture mouse and human ovaries in order to preserve oocytes in a dormant state for weeks, and therefore prolong their fertility period. Female infertility is one of the biggest societal challenges of our time. Over one in four fertility problems is unexplained, highlighting a significant knowledge gap in female reproduction. This project will provide new insights into the mechanisms by which mammalian oocytes regulate protein homeostasis and has the potential to provide new transferable knowledge to the clinic. Its relevance transcends reproductive biology, filling a critical gap in our understanding of protein quality control mechanisms in non-dividing cells and providing new angles and approaches to interpret age-related deterioration and female infertility.

WHO ARE WE LOOKING FOR?

We are looking for a highly motivated master’s student with a background in Biology, Biotechnology, or a related field, who has basic laboratory experience and speaks fluent English. The ideal candidate will be passionate about science and discovery, eager to learn and collaborate as part of a team, and respectful toward all colleagues. While not required, prior knowledge in oocyte, reproductive, or developmental biology, as well as experience in tissue culture, mouse work, microscopy, or genomics/proteomics, would be considered a plus.

HOW TO APPLY

Please send all required documents mentioned above to the PI and Adriano Bolondi - HERE. 

Systems & Synthetic Biology - MARTIN Lab

PROJECT DESCRIPTION

Mathematical Modeling | Computational Simulations | Evolution | Genotype-Phenotype Maps | Fitness Landscapes

Evolution is a process guided by two main components: natural selection and phenotypic variation through random mutations. Our group is interested in this second aspect of gaining a quantitative understanding of variation with the aim of improving evolutionary predictions. This is needed to tackle a broad range of challenges including antibiotic resistance, loss of biodiversity, or to optimize the search of functional synthetic proteins . For this, we use concepts from the fields of genotype-phenotype (GP) maps and fitness landscapes. Our work includes understanding how mutations in the genotype give rise to molecular changes in the phenotype by developing different models of GP maps (RNA secondary structure, protein self-assembly…) through computational methods. From these GP maps we can retrieve statistical properties like mutational robustness, phenotype frequency or evolvability (…) and analyze how they influence the dynamics of an evolving population. We are also interested in looking at how phenotype fitness is distributed on the GP map by building more comprehensive GPF (genotype-phenotype-fitness) maps, and retrieve other important features from them such as the distribution and accessibility of fitness peaks in the landscape. A potential master project would be making a network analysis of simple GPF maps to compare their complexity with more randomized networks. According to the student’s preference, the project could also involve developing new mathematical models of fitness distribution to make more realistic maps.

WHO ARE WE LOOKING FOR?

We are looking for a motivated student with coding experience, ideally in Python, and a solid foundation in mathematics, including probability, statistics, calculus, and algebra. The candidate may come from a background in maths, physics, or engineering with a strong interest in biology, or from biology with a strong interest in quantitative approaches.

HOW TO APPLY

Please send all required documents mentioned above to the PI and Manuela Giraud - HERE

Computational Biology of RNA Processing - GUIGÓ Lab

PROJECT DESCRIPTION - The Biodiversity Long-Read Transcriptomic Catalogue. A resource to scale and facilitate genome annotation across the eukaryotic tree of life 

Biodiversity Genomics | Genome Annotation | Long-read Sequencing | Transcriptomics | Genomics

The Earth Biogenome Project seeks to sequence and annotate all known eukaryotic genomes, but while genome assembly has advanced quickly thanks to long-read DNA sequencing, annotation lags behind. This project addresses that gap by harnessing long-read RNA sequencing (from ONT and PacBio), which allows for the reconstruction of complete transcripts, accurate isoform identification, and better annotation of noncoding regions that remain difficult for statistical approaches.

The work will focus on two key objectives: first, building an interactive catalogue of long-read transcriptomes across the eukaryotic tree of life, using ENA archive data to identify both well-represented and underrepresented clades; and second, testing the value of long-read data by comparing its contribution to structural genome annotation against that of short-read data in a chosen clade. The student will engage in ongoing projects on long noncoding RNAs or selenoproteins, with the freedom to pursue new ideas emerging from the initial analyses.

WHO ARE WE LOOKING FOR?

We are looking for a motivated candidate who combines a passion for the life sciences with strong computational skills. The ideal applicant will have a solid understanding of DNA/RNA sequencing methods and related data formats (FASTQ, FASTA, BAM, GTF/GFF), experience using Python or R for data analysis and visualisation, and confidence working with the Linux command line in high-performance computing (HPC) environments.

Additional experience with RESTful APIs or developing web interfaces will be considered a plus.

HOW TO APPLY

Please send all required documents mentioned above to the PI and Fabio Zanarello - HERE

Epigenetic Events in Cancer - DI CROCE Lab

PROJECT DESCRIPTION

Molecular Biology | Chromatin | Genome Stability | Cell Culture

Metabolism exerts powerful control over chromatin-based processes. This control is both at the level of precursor availability required for chromatin regulation and by the specific location of metabolic enzymes at particular genomic regions. Recently, we found the metabolic enzyme adenosylhomocysteinase (AHCY), which is the only enzyme in mammals that can breakdown Sadenosylhomocysteine (SAH), bound to the chromatin of pluripotent cells. SAH metabolite is the by-product of methylation reaction and provides strong negative feedback to methyltransferases. The localized compartmentalization of AHCY suggests a local function in chromatin-based processes. This project aims to investigate the local function of AHCY in chromatin and explore the therapeutic implications of this mechanism.

WHO ARE WE LOOKING FOR?

We are looking for a highly-motivated student with a background in biology (biochemistry, biotechnology, biomedicine or similar) with a strong interest in the field of molecular biology. Although prior experience is not required, prior exposure to basic lab techniques (e.g. PCR, western blot...) as well as cell culture will be valued. Most importantly, we are looking for an eager student who is willing to learn and work with us.

HOW TO APPLY

Please send all required documents mentioned above to the PI and Andrea Barrera - 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
 

Past opportunities

• CRG Summer Internship Programme
• CRG INTERNATIONAL MASTER INTERNSHIPS PROGRAMME