Project title:Genome structural plasticity: tools for single-cell spatial genomics analysis and quantification.
Background: Spatial genomics techniques (Bintu, Mateo et al. 2018, Nir, Farabella et al. 2018, Szabo, Jost et al. 2018, Finn, Pegoraro et al. 2019, Su, Zheng et al. 2020, Takei, Yun et al. 2020) enable the direct visualisation of multiple genomic loci in the nuclear space allowing the investigation of the properties of the chromatin fiber over a range of length-scales (ranging from kilobases to multiple megabases in length) one cell at the time. However, in addition to the direct visualisation of the 3D genome at varied scale and resolutions, there is a need for standardised quantitative ad-hoc analysis methods and for method to integrate spatial genomics data with others genomics information to shed light on the relationship between genome structural plasticity and genome function
Description. We are looking for a motivated PhD student with an enthusiasm to develop and implement tools to investigate the single-cell genome structure at nanoscale. Our research uses hybrid method (computational and experimental) integrating imaging techniques, as seq-OligoSTORM(Nir, Farabella et al. 2018), genomic data, structural bioinformatics, and physical theories for determining the 3D structures of genome that will contribute to a more complete characterization of genome regulatory circuits. The successful candidate will be involved in the design, development and implementation of computational tools for spatial genomic analysis and modelling. The candidate will interact and collaborate closely with other researchers in the Farabella Lab as well as with members of the other research groups within the CHT
Main Supervisor: Irene Farabella (Integrative Nuclear Architecture)
- Genetics/biology/bioinformatics/theoretical physics background or closely related fields.
- Good proficiency in Python
- Familiarity with 3D biomolecules
- Proficiency with Unix/Linux operating system
- Familiarity with imaging technologies, processing and analysis
- Familiarity with genomic data.
- Familiarity in machine learning, statistics, optimization.
- Familiarity with molecular modelling tools
How to apply. Prospective students must submit using the online form the following documents
- 2-page CV, which includes studies, expertise and achievements;
- 1-page research statement, which includes the choice of a project from the list above and a justification of the choice. Only if robustly justified, the student may signal their interest also for a second project, but there is no guarantee that this will be taken into account by the selection panel;
- a transcript of undergraduate and postgraduate studies;
- a valid IELTS certificate, obtained no more than two years before the proposed registration date.
- contact details of two referees.
For this position, ARC accepts candidatures on an ongoing basis (first-come, first-served).