What are the responsibilities and job description for the Postdoctoral Fellow, Collaborative Intelligent Systems position at Basis Research Institute?
About The Fellowship
This Basis Postdoctoral Fellowship supports postdocs joining the Collaborative Intelligent Systems project, which aims reason about a large class of collaborative behaviors, spanning a broad range of species and contexts. You will be mentored by Emily Mackevicius. In addition, you may select a co-mentor external to Basis. Applications considered on a rolling basis until the roles are filled.
PhD students interested in 3-4 month internship positions prior to graduating are encouraged to apply here.
About Basis
Basis is a nonprofit applied AI research organization with two mutually reinforcing goals.
The first is to understand and build intelligence. This means to establish the mathematical principles of what it means to reason, to learn, to make decisions, to understand, and to explain; and to construct software that implements these principles.
The second is to advance society’s ability to solve intractable problems. This means expanding the scale, complexity, and breadth of problems that we can solve today, and even more importantly, accelerating our ability to solve problems in the future.
To achieve these goals, we’re building both a new technological foundation that draws inspiration from how humans reason, and a new kind of collaborative organization that puts human values first.
About Emily Mackevicius
Emily Mackevicius is a co-founder and director of Basis Research Institute, where she leads the Collaborative Intelligent Systems group. She did her postdoctoral work studying memory-expert birds in the Aronov lab and the Center for Theoretical Neuroscience at Columbia, and her PhD work studying how birds learn to sing in the Fee lab at MIT. She is interested in how intelligent behaviors emerge, especially in distributed and recurrent systems. Her theoretical work is strongly grounded in experimental practice, currently high-resolution behavioral recordings of groups of animals foraging in environments ranging from NYC parks and subways to Arctic Alaska.
Research Focus
We study collaborative intelligent systems in the wild, focusing on how social species coordinate, communicate, and adapt in complex environments. Our work spans multiple levels of behavior, from fine-grained group foraging and navigation decisions to city-scale ecological and evolutionary dynamics. Using multimodal data (audio, video, environmental, and genetic), we develop probabilistic and dynamical systems models that investigate how communication and cooperation shape resilience in changing ecosystems. Interns will contribute to data analysis, modeling, or field data collection for our “behavioral weather station” network monitoring social species across cities.
Who We’re Looking For
By submitting your application, you grant Basis permission to use your materials for both hiring evaluation and recruitment-related research and development purposes. Your information may be processed in different countries, including the US. You retain copyright while providing Basis a license to use these materials for the stated purposes.
Read our full Global Data Privacy Notice here.
Compensation Range: $100K
This Basis Postdoctoral Fellowship supports postdocs joining the Collaborative Intelligent Systems project, which aims reason about a large class of collaborative behaviors, spanning a broad range of species and contexts. You will be mentored by Emily Mackevicius. In addition, you may select a co-mentor external to Basis. Applications considered on a rolling basis until the roles are filled.
PhD students interested in 3-4 month internship positions prior to graduating are encouraged to apply here.
About Basis
Basis is a nonprofit applied AI research organization with two mutually reinforcing goals.
The first is to understand and build intelligence. This means to establish the mathematical principles of what it means to reason, to learn, to make decisions, to understand, and to explain; and to construct software that implements these principles.
The second is to advance society’s ability to solve intractable problems. This means expanding the scale, complexity, and breadth of problems that we can solve today, and even more importantly, accelerating our ability to solve problems in the future.
To achieve these goals, we’re building both a new technological foundation that draws inspiration from how humans reason, and a new kind of collaborative organization that puts human values first.
About Emily Mackevicius
Emily Mackevicius is a co-founder and director of Basis Research Institute, where she leads the Collaborative Intelligent Systems group. She did her postdoctoral work studying memory-expert birds in the Aronov lab and the Center for Theoretical Neuroscience at Columbia, and her PhD work studying how birds learn to sing in the Fee lab at MIT. She is interested in how intelligent behaviors emerge, especially in distributed and recurrent systems. Her theoretical work is strongly grounded in experimental practice, currently high-resolution behavioral recordings of groups of animals foraging in environments ranging from NYC parks and subways to Arctic Alaska.
Research Focus
We study collaborative intelligent systems in the wild, focusing on how social species coordinate, communicate, and adapt in complex environments. Our work spans multiple levels of behavior, from fine-grained group foraging and navigation decisions to city-scale ecological and evolutionary dynamics. Using multimodal data (audio, video, environmental, and genetic), we develop probabilistic and dynamical systems models that investigate how communication and cooperation shape resilience in changing ecosystems. Interns will contribute to data analysis, modeling, or field data collection for our “behavioral weather station” network monitoring social species across cities.
Who We’re Looking For
- Researchers holding a PhD in neuroscience, cognitive science, behavioral science, computer science, artificial intelligence, machine learning, or related fields.
- Experience in the following research areas highly valued:
- Behavioral modeling using AI / machine learning / computer vision techniques
- Multi-agent animal communication
- Environmental modeling, e.g. gaussian splat models, semantic segmentation
- Strong programming skills, including experience developing novel approaches, and contributing to larger codebases.
- Demonstrated track record in scientific research, evidenced through publications, technical reports, or impactful software projects.
- Conduct independent and collaborative research focused on the Collaborative Intelligent Systems project.
- Analyze multi-agent multi-species behavioral data, including fieldwork datasets collected at Basis, as well as open-source datasets, and datasets from collaborators.
- Coordinate research with collaborators and Basis Core Tech team.
- Disseminate research findings through open-source code, academic publications, and/or presentations at leading conferences.
- Provide mentorship to junior team members and contribute to the scientific discourse through seminars, workshops, and collaborative projects.
- Full-time: This fellowship is full-time and has a duration of 2 years with possible extension.
- Location: Applicants should reside in, or be willing to relocate to, the Cambridge, MA area. This is an in-person position. You will be expected to travel periodically, funded by Basis, about once every six to eight weeks, for Basis-wide in-person events, typically in New York City.
- Salary: Competitive with leading postdoctoral fellowships.
- Start date: Immediate start possible.
By submitting your application, you grant Basis permission to use your materials for both hiring evaluation and recruitment-related research and development purposes. Your information may be processed in different countries, including the US. You retain copyright while providing Basis a license to use these materials for the stated purposes.
Read our full Global Data Privacy Notice here.
Compensation Range: $100K
Salary : $100,000
