Postdoctoral researcher in Data Analysis of Biological Images

We are seeking a Postdoctoral Research Assistant for the Gene Machines' group, led by Prof Achilles Kapanidis. The group is well known for developing single-molecule and single-cell fluorescence methods (Uphoff PNAS 2013; Robb, Sci Reports 2019; Zagajewski, Nature Comm Biol 2023, Chatzimichail, Lab-on-a-chip 2024) and applying them to proteins functioning on DNA and RNA (Stracy, PNAS 2015; Mazumder, PNAS 2020; Mazumder, eLife 2021; El Sayyed et al Mol Cell 2024). The group is also known for the development of the Nanoimager microscope and the founding of the microscopy company Oxford Nanoimaging (https://oni.bio/). The group recently moved in the new Kavli Institute for Nanoscience Discovery ( https://kavlinano.ox.ac.uk/), a vibrant environment for interdisciplinary research at the interface of physical and biological sciences, and for technology development.
This post is fixed term for 4 years
The project focuses on elucidating the mechanisms of gene transcription, by visualising the biochemical reaction, its kinetics, and key conformational changes at the single-molecule level, and in real-time within the crowded environment of a living cell. This approach will lead to new discoveries and insightful comparisons with in vitro studies and help identify new steps for antibiotic development.

To achieve this exciting goal, you will develop and apply novel data-analysis methods, (especially image analysis and time-series analysis methods) and associated software, while employing an multi-disciplinary approach that involves tight collaboration within a team of biologists, biochemists and biophysicists in the Kapanidis group. We are particularly interested in image-analysis experts who have experience in experimental work and are keen to engage both with the physical and biological aspects that underpin the images to be analysed.
The ideal candidate should possess (or soon obtain) a doctorate in Physics, Biophysics, or related field, and have experience of Python, and MATLAB. Experience in image and time-series analysis, in machine-learning, and managing large data sets is essential. Experience with parallelized data analysis, LabVIEW programming, biological systems, bacterial imaging, and fluorescence microscopy is desirable, but not essential.
You should have excellent communication skills and enjoy working within an interdisciplinary group. You will manage academic and administrative activities, develop ideas for generating research income, collaborate on reports and journal articles, and have the opportunity to teach.