Staff Scientist (Machine Learning Single-Molecule Center)
About the Job
St. Jude Children’s Research Hospital has a unified mission to use cutting-edge research to discover the origins and treatment of childhood disease, especially cancer, and to improve the lives of patients. Towards that end, at the Single-Molecule Imaging Center (SMC) in the Department of Structural Biology, we provide a critical platform to perform state-of-the-art single-molecule fluorescence imaging on biological systems relevant to medical research. We are looking for a highly motivated data scientist, that will leverage their expertise in advanced statistical concepts and modern machine learning to develop novel analysis methods that improve quantity and quality of information extracted from experimental data. The applicant will skillfully translate ideas into user-friendly implementations that can be used daily by non-experts. The successful applicant will be an integral part of the Single-Molecule Imaging Center and the larger, expanding community of data scientists and computational biologists across St Jude.
- van de Meentet al. (2014), Empirical Bayes Methods Enable Advanced Population-Level Analyses of Single-Molecule FRET Experiments. Biophys. J 106, p. 1327.
- Gopich I.V. & Szabo A. (2009), Decoding the Pattern of Photon Colors in Single-Molecule FRET. J. Phys. Chem. B 113, p.91
- Thomsen J. et al. (2020) DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning. eLife 9
- Li J. et al. (2020) Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning. Nat. Commun. 11, p. 5833.
- Links of interest for relevant software:
- Design and implement new machine learning and deep learning algorithms for the analysis of single molecule time series data, including maximum likelihood and maximum entropy methods for the modeling of complex biological systems using noisy data.
- Maintain working knowledge of relevant literature to leverage published theory and algorithms into polished implementations in e.g. MATLAB and Mathematica, with user-friendly interfaces, detailed documentation, and workflow training sessions.
- Develop working knowledge of noise sources in single molecule fluorescence recordings (including the principles of fluorescence, photophysical phenomena, and microscopy) and how they affect data interpretation.
- Present research findings in oral presentations and academic publications.
- A PhD in physics, physical chemistry, statistics, or computational science is required.
- A minimum of five (5) years of relevant and productive (combined) years postdoctoral research associate or five (5) years of combined academic experience at the postdoctoral level or above.
- Expert knowledge of machine learning algorithms, hidden Markov modeling, probability theory, and statistics with applications in the analysis of biological time series data.
- Proven experience translating theory into user friendly applications with quantified utility for real-world data.
- Willingness to develop expertise in the software development environment suited to each project, which will include but not limited to MATLAB and Mathematica.
- Expert ability to distill complex information in a clear and concise manner both verbally and in writing.
- Working knowledge of fluorescence spectroscopy, microscopy, and image analysis is a plus.
- Experience with software engineering principles, unit testing, and version control (git) is a plus.
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