Postdoctoral Research Associate - BINDS Lab

University of Massachusetts Amherst

Amherst, MA

ID: 7077592
Posted: April 23, 2021
Application Deadline: Open Until Filled

Job Description

About UMass Amherst

UMass Amherst, the Commonwealth's flagship campus, is a nationally ranked public research university offering a full range of undergraduate, graduate and professional degrees. The University sits on nearly 1,450-acres in the scenic Pioneer Valley of Western Massachusetts, and offers a rich cultural environment in a bucolic setting close to major urban centers. In addition, the University is part of the Five Colleges (including Amherst College, Hampshire College, Mount Holyoke College, and Smith College), which adds to the intellectual energy of the region.

Job Summary

Perform research in the topic of lifelong learning AI with a focus on time aware systems – systems that can take into account timing properties and draw conclusions that may be time sensitive such as anytime algorithms, periodic behavior, and separating old vs. current. The work will combine neuroscience modeling of associative and episodic memory with neural networks and symbolic methods. Meta-learning is likely to be part of the various solutions. The postdoc will need talent in presenting ideas clearly and writing clear reports and research papers. The postdoc will further help coordinate research, making sure DARPA reports are written in timely manner and great level, and will assist in developing new directions, projects and proposals. The postdoc will be available for daily meetings and collaboration with BINDS directors and students.

Job Description

Contributes to research in a highly technical Computer Science lab. This involve: developing new ideas, algorithms, analysis, software and/or hardware; designing, documenting, analyzing, modifying and debugging software; evaluating and validating software, circuits, algorithms and architectures.
Supervises research assistants.
Carries out experiments in association with students, staff and faculty.
Describes techniques and results for use in scientific papers and conferences; assists in writing reports and articles.
Contributes to the development of new research proposals.


Ph.D. in Computer Science or related field.
Capacity for excellent research, as demonstrated by publications and other professional activities, in the areas of machine learning, neural networks and some temporal directions.
Expertise in machine learning, neural networks, deep nets, autoencoders, convolution networks, and Hopfield nets.
Expertise in programming languages appropriate to the specific field of study.
Expertise with building and maintaining software systems.
Strong analytical math background.
Familiarity with theoretical computer science and engineering.
Interest in neuroscience and with memory models.
Ability to work with a minimal amount of supervision.
Ability to multi-task.
Ability to explain scientific concepts well within and outside the research community.
Experience writing research papers and preparing presentations.

Application Instructions

Applicants must submit a CV, research statement, and three (3) letters of recommendation.

Review of applications will begin immediately and will continue until the position is filled.

UMass Amherst is committed to a policy of equal opportunity without regard to race, color, religion, gender, gender identity or expression, age, sexual orientation, national origin, ancestry, disability, military status, or genetic information in employment, admission to and participation in academic programs, activities, and services, and the selection of vendors who provide services or products to the University. To fulfill that policy, UMass Amherst is further committed to a program of affirmative action to eliminate or mitigate artificial barriers and to increase opportunities for the recruitment and advancement of qualified minorities, women, persons with disabilities, and covered veterans. It is the policy of the UMass Amherst to comply with the applicable federal and state statutes, rules, and regulations concerning equal opportunity and affirmative action.


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