Assistant/Associate Professor - Data Science
About UMass Amherst
The College of Information and Computer Sciences was created in 2015 and is rapidly building on the foundation of our strong computer science program. Our faculty are remarkably collaborative and are involved in interdisciplinary research with many other departments and universities. We have a long history of working with industry, and the new centers in data science, cybersecurity, smart and connected society, and computational social science, are aggressively expanding the range of industry partners and funding. For more information, visit https://cics.umass.edu.
The University of Massachusetts Amherst (http://www.umass.edu), the flagship campus of the University of Massachusetts system, is a nationally ranked public research university and home to over 22,000 undergraduate and 6,000 graduate students. The 1,430 acre campus is located in the scenic Pioneer Valley of western Massachusetts, with easy access to Boston and New York City. UMass Amherst, along with Amherst, Hampshire, Mount Holyoke and Smith Colleges, is a member of the Five College Consortium and the Academic Career network.
Our college is highly supportive of junior faculty, providing both formal and informal mentoring. Many of our faculty are involved in interdisciplinary research, working closely with other departments including statistics/mathematics, linguistics, electrical and industrial engineering, biology, physics, behavioral sciences, economics, political science, and nursing, as well as new green initiatives. Amherst, a historic New England town, is the center of a vibrant and culturally rich area that includes five colleges. For more information about our college, visit https://cics.umass.edu.
The College of Information and Computer Sciences at the University of Massachusetts Amherst invites applications for tenure-track faculty at the Associate and Assistant Professor levels in the broad area of Data Science. Exceptional candidates at other ranks may be considered. All areas of data science will be considered, including machine learning, statistics, optimization, deep learning, reinforcement learning, game theory, fairness/accountability, systems for data science, theory for data science, as well as applications to computational economics, agriculture, ecology, education, biomedicine, images, and text.
Applicants must have a Ph.D. in Computer Science or a related area, and should show evidence of exceptional research promise.
Starting Date: January 20, 2019 or September 1, 2019. Other start dates (i.e. Spring 2020) may be considered for exceptionally qualified candidates.
Salary will be highly competitive and commensurate with qualifications and experience. Inquiries and requests for more information can be sent to: firstname.lastname@example.org.
The university is committed to active recruitment of a diverse faculty and student body. The University of Massachusetts Amherst is an Affirmative Action/Equal Opportunity Employer of women, minorities, protected veterans, and individuals with disabilities and encourages applications from these and other protected group members. Because broad diversity is essential to an inclusive climate and critical to the University's goals of achieving excellence in all areas, we will holistically assess the many qualifications of each applicant and favorably consider an individual's record working with students and colleagues with broadly diverse perspectives, experiences, and backgrounds in educational, research or other work activities. We will also favorably consider experience overcoming or helping others overcome barriers to an academic degree and career.
All applicants should attach a cover letter, curriculum vitae, research statement, and a statement of teaching interests.
Applicants at the Assistant Professor level should submit the names and contact information for three references and links to two papers that best represent their research/experience. Applicants at the Associate Professor level should submit the names and contact information for four references and links to three papers that best represent their research/experience.