International PhD Programme research topics
When you apply for the EMBL International PhD Programme, you are asked to select two EMBL research units 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.
The following Group Leaders at EMBL-EBI are hoping to recruit PhD students in the ongoing round.
Analysis of protein and RNA sequence
Work in the Bateman 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 group has produced the Pfam, Rfam, TreeFam and MEROPS data resources to collect and analyse these families of molecules, and has published a large number of novel protein domains and families.
Evolution of Cellular Networks
The Beltrao 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. They collaborate with experimental groups, focusing on two areas: the evolution of chemical--genetic interactions in different species and individuals; and the function and evolution of post-translational regulatory networks.
Functional genomics research
The Brazma research group complements the Functional Genomics service team, 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.
Computational microbiome research
The Finn research group focuses on developing computational approaches for the reconstruction of genomes from metagenomes, thereby enriching the tree of Life. The group will also investigate the distribution of microbes and functions in different environments, and probe the functions of so-called ‘microbial dark matter’.
Evolution of transcriptional regulation
The Flicek research group develops 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.
Computational cancer biology
The Gerstung group develops statistical models and bioinformatics tools for understanding cause and consequence of cancer genomes. They create models for relating different layers of genomic, molecular and clinical data to extract the precise connections among variables to understand the connection of genotype and phenotype. They also develop biostatistical models and informatics tools for predicting outcome based on comprehensive high-dimensional datasets. They also study the evolutionary dynamics of cancer, developing accurate biooinformatics tools to accurately detect subclonal mutations and reconstruction of phylogenies.
Evolutionary tools for genomic analysis
The Goldman group develops new evolutionary models and methods; provides these methods to other scientists via stand-alone software and web services; and applyies such techniques to biological questions of interest. The group participates in comparative genomic studies, including the analysis of next-generation sequencing data, which promises great gains in understanding genomes and brings with it many new challenges.
Computational and evolutionary genomics
The Marioni group develops effective statistical and computational methods for analysing the vast amounts of data generated in high-throughput experiments, and collaborates with experimental colleagues to apply these methods to a range of biological questions. They focus 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.
Proteins: structure, function and evolution
The Thornton group uses computational techniques to explore how enzymes perform catalysis, and develop novel software tools to characterise enzyme mechanisms and navigate the catalytic and substrate space. They also investigate the evolution of these enzymes to discover how they can evolve new mechanisms and specificities. To improve the prediction of function from sequence and structure, and to enable the design of new proteins or small molecules with novel functions, the group integrates heterogeneous data with phylogenetic relationships within protein families, based on classification data derived by colleagues at University College London (UCL). They collaborate with experimental biologists at UCL to elucidate the molecular basis of ageing in different organisms, analysing functional genomics data from flies, worms and mice and, by developing new software tools, relating these observations to effects on life span.
Our group develops bioimage analysis tools that blend continuous mathematical models and computer vision (e.g., learning-based) algorithms. We are interested in flexible contour representations that allow identifying and precisely characterizing biological objects in a large variety of image data. These models can then be applied to quantify phenotypical variations at the single-cell or single-individual level in experiments ranging from genome-wide knockout screens to population dynamics studies.
We are recruiting students who have a good background in computer vision and image processing, with interests in optimisation, approximation and sampling theory. Our group will start at EMBL-EBI on 15 September 2018.
EMBL-EBI - NIHR Cambridge Biomedical Research Centre collaboration
Some projects are part of the EMBL-EBI - NIHR Cambridge Biomedical Research Centre collaboration.
The European Bioinformatics Institute (EMBL-EBI) and NIHR Cambridge Biomedical Research Centre (BRC) are building on the collaborative relationship between the two institutes. Graduate students will be full members of the EMBL International PhD programme, will be registered as students of the University of Cambridge, and will work across both institutes on collaborative projects with supervision from members of faculty from EMBL-EBI and from the Cambridge BRC. Projects will be broadly related to translational clinical research projects involving human subjects.
The National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (BRC) is a partnership between University of Cambridge and Cambridge University Hospitals (CUH). It was established in 2007, and has recently been re-designated as a NIHR Biomedical Research Centre with an award of £114.3m over the next five years. The Cambridge Biomedical Research Centre is based on the Cambridge Biomedical Campus, which combines on a single site scientific research in world-class institutes, patient care in NHS hospitals, and drug discovery in pharmaceutical companies including AstraZeneca and GlaxoSmithKline (GSK). Over the next five years the BRC will undertake translational research in the fields of cancer, cardiovascular and respiratory disease, dementia, disorders of the nervous system and mental health, infections and their resistance to antibiotics, obesity and diabetes, bone disease, digestive disorders and the effect of nutrition, diet and lifestyle on health, transplantation and the use of stem cells to repair tissues, and women's and children’s health.
EBI Group Leader
BRC Group Leader
|Evangelia Petsalaki||Antonio Vidal-Puig||
"Cell signalling and metabolic pathways in brown adipose tissue differentiation".
In this project, the successful candidate will use multi-omics data from human pluripotent stem cells to study the development of brown adipose tissue and white adipose tissue browning with applications in treatments for obesity and type II diabetes. There is also the option to contribute in the wet lab in the generation of these datasets if there is interest.