Research Assistant & Associate in Phage Research at the University of Cambridge
The Department of Engineering at the University of Cambridge is inviting applications for a Research Assistant/Associate to join Dr. Somenath Bakshi’s research group. This exciting project focuses on developing quantitative, physiology-aware models of bacteriophage infection by integrating cutting-edge experimental and computational approaches.
The research combines high-throughput single-cell microscopy, microfluidics, quantitative image analysis, and mathematical modelling to better understand how bacterial physiology influences phage infection dynamics. The findings will contribute to the development of more effective phage-based antimicrobial therapies.
The successful candidate will work with a unique experimental platform developed by the Bakshi Lab that enables time-resolved analysis of individual bacterial cells throughout the phage infection process.
Key Responsibilities
- Design and perform quantitative single-cell bacteriophage infection experiments.
- Analyze large-scale microscopy datasets.
- Develop computational methods to extract infection dynamics from individual bacterial cells.
- Integrate experimental data with mathematical and statistical models of phage-host interactions.
- Collaborate with an interdisciplinary team of microbiologists, engineers, and computational scientists.
- Present research findings at conferences and scientific meetings.
- Publish research outcomes in high-impact peer-reviewed journals.
- Participate in seminars, journal clubs, and other academic activities.
Qualifications & Skills Required
Applicants should have obtained or be close to completing a PhD in one of the following disciplines:
- Microbiology
- Systems Biology
- Bioengineering
- Biophysics
- Biotechnology
- Engineering
- Or a closely related field
Experience in one or more of the following areas is desirable:
- Bacteriophage biology
- Microbiology
- Quantitative microscopy
- Single-cell imaging
- Image analysis
- Mathematical modelling
- Machine learning
- Microfluidics
- Computational biology
- Statistical data analysis
For more phage-related research opportunities, check here
