Applications are invited for a Postdoctoral Research Associate (PDRA) position to work on the development and applications of a microfluidic platform for analysing protein aggregation, with a focus on high-throughput data analysis and management, a project funded by the Michael J. Fox Foundation. The PDRA will work in the group of Professor Tuomas Knowles in the Yusuf Hamied Department of Chemistry at the University of Cambridge. The aim of this interdisciplinary project is to develop an approach for detecting biomarkers that allow early diagnosis of Parkinson's disease, provide a means to track disease progression, and support the development of future therapeutics. A particularly attractive route to achieve this objective is to directly measure the seeding activity of misfolded and aggregated forms of the central protein driving Parkinson's disease (PD), alpha-synuclein. Yet, the ability to measure and quantify the seeding activity of pathological aggregates in clinical samples has remained elusive. Here, we propose to address this challenge by developing a quantitative and practical seeding assay in digital format which allows better quantification. Success with this project will open a platform and path towards quantitative PD biomarker detection. The project aims to develop a novel toolbox enabling the digital quantification of single aggregates supporting PD diagnosis and following disease progression. To this end, the PDRA will manage and implement novel data analysis methods for the data collected by the high-throughput microfluidic platform, in order to complete the biophysical toolkit that will allow real-time characterisation of alpha-synuclein aggregate at a single aggregate resolution.
Qualifications
* Ph.D. in a quantitative field of science (biophysics, physics, engineering, chemistry etc.)
* Experience in quantitative modeling of protein aggregation kinetics, amyloid formation, or molecular self-assembly processes
* Programming and scientific computing skills (e.g. Python, MATLAB, R, or similar)
* Experience in managing and working with large sets of clinical data
* Experience in applying machine learning or AI-based approaches to scientific image or data analysis
Applicants will be expected to work collaboratively in an interdisciplinary environment, interact and communicate effectively with a team of scientists with a wide range of expertise in biophysics and physical chemistry.
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