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Tufts University has openings for three post-doctoral researchers to engage in cross-cutting projects focusing on the development of “precision” models and algorithms with applications in learning science, nutrition, and medicine. These three domains are seeing an explosion in the types and quantities of information that can be collected to provide insight into various aspects of human physiology and psychology.    Practitioners in these fields are eager to use these data to understand the dynamics of a wide variety of processes taking place both within and between people and make timely and accurate predictions at the scale of the individual. The characteristics of the data however render traditional analysis methods, largely concerned with population level statistics, inadequate. Many of these challenges arise from issues of heterogeneity.  Some sources such as wearables provide data continuously.  Others yield measurements at only a few discrete points in time (e.g., biomarkers derived from saliva samples) while audio and video are examples of data that may be provided in noncontiguous intervals of varying length.  Most of these data are only indirectly related to the phenomena of interest with no explicit model linking the two as is the case for student work in the context of learning, electronic medical records for nutrition or medicine, or the results of questionnaires in all applications of interest.  Finally, although the quantity of data collected about any one individual may be relatively large, practical considerations make the number of participants associated with most studies relatively small, on the order of tens at most making these simultaneously “big” and “small” data problems.      

While learning, nutrition, and medicine each possess unique characteristics, the common challenges just described strongly suggest that an integrated approach to “precision analytics” will provide a fruitful path forward.  Thus, we are looking for three PhD-level scientists with interests in pioneering rigorous and at the same time useful solutions to the problems outlined above.  Each researcher will lead the effort in a specific domain, and the group as a whole will work collaboratively to exploit underlying commonalities across the different disciplines.   Successful candidates will have a PhD in a quantitative discipline such as applied mathematics, statistics, signal processing, machine learning, or physics with a track record of high-quality publications in relevant journals and peer reviewed conferences.  We seek individuals who will advance the state of the art in machine learning, data science, artificial intelligence etc. in a manner that also support the research goals and interests of our application domain partners drawn from

• The Tufts Center for Applied Brain and Cognitive Sciences
• The Tufts Institute for Research on Learning and Instruction
• Jean Mayer USDA Human Nutrition Research Center on Aging
• The Stuart B. Levy Center for Integrated Management of Antimicrobial Resistance

For more information about this position, please email Prof. Eric Miller at eric.miller@tufts.edu. 

Interested candidates should provide Prof. Miller with a copy of their CV, list of references, cover letter, and copies of relevant articles, theses, technical reports etc.