This is a full-time, fixed term (four years) position on Crick terms and conditions of employment.
The research group
Our group is interested in host and pathogen interactions in tuberculosis with a focus on innate immunity and phagocytosis. We are interested in understanding how M. tuberculosis survive within host cells and the mechanisms that host cells use to eliminate intracellular M. tuberculosis.
Project summary
In our lab, we study how host cells environments dictate intracellular bacteria replication or control and how antibacterial chemotherapies impact efficacy. We are interested in developing new technologies to dissect the complex dynamics of mycobacterial infection across scales ranging from single bacteria, single host cells, 3D in vitro models to infected hosts. We have developed new imaging technologies to visualise the interface between the host and the pathogen.
Postdoctoral Training Fellows are expected to lead their own projects, contribute to other projects on a collaborative basis (both in the lab and with external collaborators) and guide PhD students in their research. The ability to work in a team is essential.
Key experience and competencies
The post holder should embody and demonstrate our core Crick values: bold, imaginative, open, dynamic and collegial, in addition to the following:
Essential
- PhD in cell biology or in the final stages of PhD submission
- Track record of writing papers as evidenced by publications or submitted manuscripts in referred journals
- Evidence of data presentation at scientific meetings
- Excellent communication skills, particularly in communicating quantitative concepts to biologists
- Ability to work independently and also capable of interacting within a group
- Demonstrate continuous integrity, positivity and motivate others to do the same
Desirable
- Experience with relevant software tools such as R, Python, or MATLAB as well as relevant machine learning frameworks
- Experience in statistical data analysis, and expertise in areas such as experimental design, linear/nonlinear models, data mining, Bayesian methods, and statistical learning
- Multi modal imaging and mammalian cell biology
- Hands-on experience in imaging
- Strong understanding of image analysis
