Post Doctoral Research Associate – Collective Intelligence Lab
Humanities & Social Sciences
Niccolo Pescetelli, Humanities & Social Sciences
The Collective Intelligence Lab at the New Jersey Institute of Technology (NJIT) is seeking a full-time postdoctoral research associate with expertise in computational social science to join a funded research project led by Dr. Niccolo Pescetelli (NJIT).
This project brings together experimental psychologists, computer scientists, and network scientists to achieve two goals:
G1: understand the principles of collective adaption, and
G2: apply these principles to create a new generation of hybrid human-machine systems capable of learning in and adapting to dynamic environments.
The postdoctoral research associate will work mainly on achieving the first goal by a mix of modeling and experimental work using groups of human volunteers.
Y1: The postdoctoral research associate will work with existing models of cognitive control, learning, and decision-making at the individual level and apply them to collectives. Y1 intends to build strong theoretical foundations for the project.
Y2: In Y2, the postdoctoral research associate will perform behavioral online group experiments on human volunteers to test the initial models.
The postdoctoral research associate will be given ample opportunity to participate in activities linked to the second goal, G2. The creation and analysis of dynamical models of collective adaptation and their application to real-world networks. The position starts as early as September 1st, 2022, and is expected to be filled no later than December 1st, 2022. The position is for two years but may be extended if additional funding is secured during the course of the project. In addition, the PI will work with the postdoctoral research associate toward securing independent funds for their future career, especially targeting NSF and other national grants.
The successful candidate will be expected to fulfill the following essential functions:
- Lead the preparation, execution, and analysis of the experimental portion of the project in collaboration with the PI.
- Contribute to or lead lab publications, conference submissions, as well as grant proposals.
- Act as a mentor to diverse and ambitious students in the lab, as well as collaborate effectively with colleagues and support staff.
- Through mentorship and strategic planning with the PI, develop additional skills and experience valuable to long-term career goals.
- While the functions of the successful candidate will be primarily focused on the goals of the research project, there will be opportunities to develop independent projects as well as participate in other ongoing lab initiatives.
- Mentoring: The successful candidate will receive direct guidance in mentoring students, the development of new scientific skillsets, and career planning. In addition, funding is available to attend workshops and conferences. Part of the project planning will include a roadmap for generating products that correspond with the candidate’s long-term career goals.
- Ph.D. in at least one of the following or related fields: behavioral science (e.g. experimental psychology, management, human factor, etc), network science, complex systems modeling, computational social science, computer science.
- A strong interest in conducting collaborative and interdisciplinary research. In particular, candidates should be willing to engage in collaborative activities outside of their primary discipline.
- A strong track record of publications.
- Knowledge of at least one of the following languages: Python, R, Matlab.
- At the university’s discretion, the education and experience prerequisites may be exempted where the candidate can demonstrate to the satisfaction of the university, an equivalent combination of education and experience specifically preparing the candidate for success in the position.
- Although prior experience in experimental work is not required, the successful candidate should demonstrate an interest in engaging with behavioral experimental work and online group experiments.
- Knowledge and experience in using modern statistical tools (e.g. GLMMs), machine learning techniques (e.g. reinforcement learning, classifiers), agent-based modeling methods, and/or mathematical modeling approaches is a strong advantage.
Special Instructions to Applicants:
- Potential candidates are strongly encouraged to contact Niccolo Pescetelli ([email protected]) to discuss the details of the project and learn more about the position.
- Applications will be reviewed as they are received. The application period will remain open until filled.
- As an EEO employer NJIT is committed to building a diverse and inclusive teaching, research, and working environment and strongly encourages applications from individuals with disabilities, minorities, veterans, and women.
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recognize the cultural and personal differences. We strive to cultivate an inclusive campus culture that promotes excellence among
our faculty, staff and students. Building a robust and diverse community is critical to NJIT’s continuing status as a premier institution
of higher education and a leading polytechnic university.
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