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 a provisional list of available PhD projects at EMBL-EBI which are available during the Spring recruitment 2017 round.
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.
Contact the Bateman research group
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.
Contact the Beltrao research group
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.
Contact the Brazma research group
Developing computational methods for representing genetic variation, and using them to study bacteria and parasites.
Our group develops computational methods for representing the full breadth of genetic variation. We work closely with species experts and try to use concrete exemplars of types of challenging variation to focus our models and methods. We also apply these methods to real data, for example M. tuberculosis, S. aureus, E. coli and P. falciparum. Our translational work focuses on applying whole genome sequencing to pathogens in a clinical setting. We have developed a rapid, lightweight app for predicting antibiotic resistance given sequence data from a sample of S. aureus or M. tuberculosis. To enable strain and resistance surveillance, we are building online genome graph databases of pathogen variation.
Contact the Iqbal research group
Creating predictive and conditional whole cell signaling models
Our lab focuses on studying human cell signaling with the aim to create predictive and conditional whole cell signaling models. We plan to use these models to gain insight into basic cell functions and disease mechanisms in order to aid the design of therapeutic approaches or biomarker discovery for patients with specific proteome, expression or genome profiles.
Contact the Petsalaki research group
Small molecule metabolism in biological systems
The Steinbeck Research Group aims to understand how the exposome and metabolome interact. This entails the analysis of advanced multi-omics phenotypic data sets, with an emphasis on metabolomics and with chances of stepping into systems biology scale models. The group has a track record in different areas of cheminformatics and bioinformatics related to metabolism, such as structure elucidation, metabolic model reconstruction, prediction of spectroscopic and other physicochemical properties represented in chemical graphs and machine learning methods applied to mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectra data among others. Students and researchers at the group benefit from the close interaction with the Steinbeck Service team, which is one of the world leaders in the provision of metabolomics and small molecule data and metabolomics standards, and from its extended network of collaborators in metabolism related research.
Contact the Steinbeck research group