We are a young, unconventional group looking for highly motivated post-doctoral fellows with research interests in computational modeling applied to COVID-19 and other infectious diseases. Our lab develops multi-disciplinary methods and tools on computational epidemiology, clinical machine learning, and biomedical data visualization. We are currently building up a comprehensive research program on infectious disease modeling built around a “real-life” outbreak simulator called Operation Outbreak (OO) that uses proximity sensing on mobile devices to spread a virtual pathogen among participant’s smartphones (https://operationoutbreak.org). The primary use of OO has been experiential learning about infectious diseases at middle and high school levels, but the data generated by OO simulations is highly realistic, in particular with regards to behavioral patterns affecting disease spread, see Colubri et al. 2020 (https://pubmed.ncbi.nlm.nih.gov/32905783) and Specht et al. 2022 (https://www.medrxiv.org/content/10.1101/2022.02.04.22270198v2). The aim is to use synthetic OO datasets (including simulated pathogen sequences) as ground truth to train and validate outbreak reconstruction algorithms and risk prediction models. We are looking for candidates with strong background in computational biology, bioinformatics, or machine learning, who are interested in creating novel predictive models that integrate heterogeneous datasets through the application of genetic epidemiology, Bayesian inference, Markov Chain Monte Carlo, coalescent theory, social network analysis, and graph theory, with the candidate also welcome to bring their own methodological approach to the lab. We are also establishing collaborations with other groups at UMass Chan and the Broad Institute of MIT and Harvard, in the topics described above, but also on projects that would involve immunology modeling and analysis of viral genomics sequence data. For more information about this position, please check the lab’s website https://co-labo.org and this medium post: https://bit.ly/3c5DAvS