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.
For any inquiries regarding the next recruitment round, please contact ROffice@ebi.ac.uk.
Lees research group (incoming)
Pathogen informatics and modelling
- Computational modelling, particularly linking genomics and epidemiological data.
- Bioinformatics algorithms, particularly designing highly-parallelised code run on GPUs.
- Studies of genomic epidemiology and within-host diversity.
Full Group website is currently under development and will be available soon. Meanwhile please contact the EMBL-EBI Research Office for further information (ROffice@ebi.ac.uk).
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.
The Petsalaki group studies human cell signaling with the aim of creating predictive and conditional whole-cell signaling models. Using these models, we seek to gain insights 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.
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.