Assistant Professor (Tenure Track) - Statistics/StatML and Societal Problems
Carnegie Mellon University: Heinz College
Location: Pittsburgh, PA
Open Date: Nov 15, 2021
Deadline: Jan 17, 2022 at 11:59 PM Eastern Time
CMU is using Interfolio's Faculty Search to conduct this search. Applicants to this position receive a free Dossier account and can send all application materials, including confidential letters of recommendation, free of charge.
Assistant Professor (Tenure-Track) in Statistics/ML and Data Analytics The Heinz College at Carnegie Mellon University invites applications for the 2022-23 academic year for tenure-track positions at the assistant professor level for candidates who are trained (or cross-trained) in statistics or statistical machine learning. The College is home to the University’s School of Information Systems and Management and its School of Public Policy and Management – a unique co-location of Information Systems, Public Policy, and Management. Heinz College is a multi-disciplinary college with a collegial and supportive culture.
Successful candidates will have outstanding research abilities and be committed to achieving excellence in teaching. The College houses several research centers with deep strengths in analytics that encourage and enable societal and policy impact (e.g., our Block Center for Technology and Society and our Smart Cities Institute) and has close ties via research centers, student advising, and faculty collaborations with the Department of Statistics and Data Science, Department of Machine Learning, Department of Social and Decision Sciences, Tepper School of Business, and the College of Engineering.
The ideal scholar will have strong methodological interests and be able to benefit from and complement our large group of faculty with applied and methodological interests in the data sciences. Applications are encouraged regardless of methodological area though synergies with existing strengths in fairness, accountability, transparency, and equity (FATE) of data analysis/algorithms, data analysis for good, and causal inference are of particular interest. The ideal candidate will also have strong interest in and experience with research regarding societal problems. Applications are welcome regardless of problem domain, though synergies with existing application areas of strength at Heinz will be considered. These include the future of work, smart and connected communities, health, transportation, urban systems, crime, energy, and the environment.
The candidate must have obtained, or expect to obtain shortly, a PhD or equivalent degree in Statistics, Statistical Machine Learning, or a related field.
We will begin formally reviewing applications on December 17 and strongly encourage you to complete your application by then. We will continue to accept applications until January 17, 2022.
Carnegie Mellon is committed to increasing the diversity of its community on a range of intellectual, cultural and social dimensions as we recognize the intrinsic relationship between diversity and excellence in all our endeavors. We welcome faculty applicants who will contribute to this diversity through their research, teaching and service and encourage applicants to include information regarding their contributions or planned contributions on this dimension. Carnegie Mellon University is committed to addressing the family needs of faculty, including dual career couples and single parents. We are also interested in candidates who have had non-traditional career paths or who have taken time off for family or other reasons.
More information about Heinz and its research and education programs can be found at https://www.heinz.cmu.edu/.
Carnegie Mellon University Heinz College,
5000 Forbes Ave.,
Pittsburgh, PA 15213
Successful candidates will have outstanding research abilities and be committed to achieving excellence in teaching. The ideal scholar will have strong statistics and/or statistical machine learning interests and be able to benefit from and complement our large group of faculty with applied and methodological interests in the data sciences. Applications are encouraged regardless of problem domain, though synergies with existing strengths at Heinz will be considered. These include the future of work, smart and connected communities, health, transportation, urban systems, energy, and the environment. The candidate must have obtained, or expect to obtain shortly, a PhD or equivalent degree in Statistics, Machine Learning, or a related field.
Applicants should submit all materials electronically via the Interfolio, including a cover letter, vita, statement of research objectives and aspirations, one-page summary of teaching philosophy and experience, statement of expected contributions regarding diversity, equity, and inclusion, and up to two recent research papers, and also arrange for submission of three letters of reference.
Equal Employment Opportunity Statement
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