Faculty Recruiting Support CICS

Research Positons

The UMass Amherst TRIPODS Institute for Theoretical Foundations of Data Science invites applications for postdoctoral fellowship positions in data science. Specifically, we seek candidates who work in areas broadly related to the UMass Amherst TRIPODS themes, namely:  statistical performance and data acquisition in interactive data collection; algorithms and computational models for processing large amounts of data; model robustness, approximate inference and uncertainty quantification. The Principal Investigators are Patrick Flaherty (Mathematics & Statistics, UMass),  Markos Katsoulakis  (Mathematics & Statistics, UMass), Arya Mazumdar (Data Science Institute, UCSD), Andrew McGregor (Computer Science, UMass), and Barna Saha (Industrial Engineering & Operations Research, UC Berkeley). For more information on the mission, researchers, and the research directions of the Institute please visit our website, tripods.cs.umass.edu. 

This Postdoctoral Research Associate position is associated with the National Science Foundation funded Engineering Research Center on Quantum Networks.  The focus of the center is very broad and includes research on quantum networks, quantum communications, quantum information theory as it applies to networks, distributed quantum computing, and distributed quantum sensing. The focus of the postdoc position is to perform research on one or more of the following topics: modeling & analysis, design, and optimization & of distributed quantum systems. 

We are inviting applications for a postdoctoral position in machine learning for healthcare in the Information Fusion Lab at UMass Amherst. Our project, 4Thought, aims to address one of the biggest needs in Alzheimer's disease research, an ability for the early diagnosis of this disease.   To this end, we are developing novel diagnostic technology on brain structural MRIs, cognitive test scores and biomarkers. The postdoctoral researcher will be part of a team developing cutting-edge techniques for Alzheimer's Disease forecasting, using hybrid deep learning methodology that leverages complex, multimodal data and domain knowledge.