International PhD Programme research topics
When you apply for the EMBL International PhD Programme, you are asked to select two EMBL research groups and to indicate up to four research areas that interest you. A variety of backgrounds - such as biology, chemistry, computational science, mathematics and statistics - are relevant to PhD projects at EMBL-EBI. As well as purely computational projects, there may also be possibilities to incorporate some experimental biology in collaborating laboratories.
Here, we show research groups that are currently accepting PhD students at EMBL-EBI. You can find other EMBL research units on the EMBL website, and browse all EMBL research groups in our Research at a Glance brochure.
Analysis of protein and RNA sequence
The work in Dr Alex Bateman's research group centres on the idea that there are a finite number of families of protein and RNA genes. The group endeavours to enumerate all of these families to gain an understanding of how complex biological processes have evolved from a relatively small number of components. The Bateman group has produced a number of widely used biological database resources such as Pfam, Rfam, TreeFam and MEROPS to collect and analyse these families of molecules, and has published a large number of novel protein domains and families of particularly high interest.
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Evolution of Cellular Networks
Dr Pedro Beltrao's group seeks to understand the function and evolution of cellular networks by exploring how genetic variability is propagated through molecules, structures and interaction networks to give rise to phenotypic variability. The group focuses on two areas: the evolution of chemical--genetic interactions in different species and individuals; and the function and evolution of post-translational regulatory networks. There is a strong emphasis in collaborating with experimental groups both for data acquisition and follow-up studies.
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Functional genomics research
Dr Alvis Brazma's research group complements the Functional Genomics service team, and focuses on developing new methods and algorithms and integrating new types of data across multiple platforms. The group is particularly interested in cancer genomics and transcript isoform usage, and collaborates closely with the Marioni group and others throughout EMBL.
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Evolution of transcriptional regulation
Dr Paul Flicek's research group focuses on computational models for genome annotation and evolution based on models incorporating DNA-protein interactions, epigenetic modifications, and the DNA sequence itself. The group is also interested in the large-scale infrastructure required for modern bioinformatics including storage and access methods for high-throughput sequencing data.
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Evolutionary tools for genomic analysis
Dr Nick Goldman's research group centres on three main research activities: developing new evolutionary models and methods; providing these methods to other scientists via stand-alone software and web services; and applying such techniques to tackle biological questions of interest. We participate in comparative genomic studies, both independently and in collaboration with others, including the analysis of next-generation sequencing (NGS) data. This vast source of new data promises great gains in understanding genomes and brings with it many new challenges.
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Computational and evolutionary genomics
Dr John Marioni's research group develops effective statistical and computational methods for analysing the vast amounts of data generated in high-throughput experiments. To gain a deeper understanding of complex biological processes such as gene regulation, the group develops computational methods for interrogating high-throughput genomics data. Their work focuses primarily on modelling variation in gene expression levels in different contexts: between individual cells from the same tissue; across different samples taken from the same tumour; and at the population level where a single, large sample of cells is taken from the organism and tissue of interest. Working with experimental colleagues within and beyond EMBL, the group applies their methods to biological questions ranging from the regulation of mammalian gene expression levels to the brain development in a marine annelid.
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Dr Julio Saez Rodriguez's research group creates mathematical models that integrate a range of data (from genomic to biochemical) with various sources of prior knowledge, with an emphasis on providing both predictive power of new experiments and insights into the functioning of the signaling network. Working closely with experimental colleagues, the group combines statistical methods with models describing the mechanisms of signal transduction either as logical or physico-chemical systems. They develop new tools, integrate them with existing resources and use them to explore how signalling is altered in human disease. The aim is to predict effective therapeutic targets.
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Statistical genomics and systems genetics
Dr Oliver Stegle's research group uses computational approaches to unravel the genotype--phenotype map on a genome-wide scale. Their work focuses on the development and use of statistical methodology to dissect the causes of molecular variation. The group has shown how comprehensive modelling can greatly improve the statistical power to find genetic associations with gene expression levels and provide for an enhanced interpretation of the interplay between genetic variation, transcriptional regulation and molecular traits. The address these methodological questions in the context of close collaborations with experimental groups, where they apply novel statistical tools to study molecular traits in model organisms, plant systems and biomedical applications.
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Small molecule metabolism in biological systems
Dr Christoph Steinbeck leads the Cheminformatics and metabolism service team, which runs a number of key services and develops algorithms to: process chemical information; predict metabolomes based on genomic and other information; determine the structure of metabolites by stochastic screening of large candidate spaces; and enable the identification of molecules with desired properties. This requires algorithms based on machine learning and other statistical methods for the prediction of spectroscopic and other physicochemical properties represented in chemical graphs. Dr Steinbeck also has a research group, which focuses on the understanding of the small-molecule metabolism of living organisms. The group is interested in the analysis of metabolomics experiments, including methods for computer-assisted structure elucidation of biological metabolites and metabolic pathways. They develop and maintain chemistry-related databases of biological interest, and develop machine-learning methods for the prediction of mass (MS) and nuclear magnetic resonance (NMR) spectra for use in rereplication and structure elucidation. The methods and algorithms developed in the group are available through an open-source library for structural chemo- and bioinformatics.
Contact Steinbeck research group