We are seeking a Postdoctoral Research Assistant to work in a collaborative project between the Department of Physics (Gene Machines’ group, led by Prof Achilles Kapanidis) and the Oxford Big Data Institute (Computational Phenotyping group, led by Dr Christoffer Nellaker). The Kapanidis group is well known for developing single-molecule fluorescence methods (Holden Nature Meth 2011; Gilboa Biophys J 2019) and applying them to DNA/RNA polymerases (Stracy, PNAS 2015; Duchi, Mol Cell 2016; Dulin Nature Comm 2018; Mazumder PNAS 2020) and to biomedically important microbial targets (Robb et al, Sci Rep, 2019). The Nellaker group is well known for facial recognition for rare diseases (Ferry et al, eLife, 2014), fairness of deep learning for biology (Glastonbury et al, ML4H, NeurIPS 2018; Alvi, et al, ECCV, 2018) and for histology image analysis (Ferlaino et al, MIDL, 2018)
The overall project is funded by Oxford Martin School and is a wider collaboration with colleagues at the John Radcliffe Hospital, with the central aim being the development of rapid detection of antimicrobial resistance in clinical samples. You will develop software tools and algorithms to collect and analyse large data sets generated from single-cell studies performed using living bacteria cultures and clinical samples with bacterial pathogens; much of the analysis will involve machine-learning approaches. You will be working as a member of an interdisciplinary team, which will include a biophysicist and a microbiologist. 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.
The ideal candidate should possess (or soon obtain) a doctorate in Physics, Biophysics, Computer Science, Computer Vision, Biomedical Engineering or related field, and have knowledge of C/C++, Python, and MATLAB or R. Experience in image analysis, time-series analysis, and data-storage solutions is essential. Extensive experience in machine-learning, artificial intelligence approaches and practical application of deep learning models for computer vision is essential. Experience in working within high performance computing environments is essential. Experience with parallelized data analysis software, automation algorithms, LabVIEW programming, fluorescence microscopy, and cellular imaging is desirable but not essential. You should also have a strong publication record, excellent communication skills and able to work effectively within an interdisciplinary group.
Please direct enquiries to Prof Kapanidis email@example.com
Only applications received before midday 5 February 2021 can be considered. You will be required to upload a statement of research interests, CV, copies of two representative publications and details of three referees as part of your online application.