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. Fellowships will start in March 2025, for two years with possibility of extension. We will begin reviewing applications on Nov 20, 2024, and hope to extend offers by Dec 13, 2024.
PhD students interested in 3-4 month internship positions prior to graduating are encouraged to apply here.
Basis Research Institute 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.
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.
Many of humanity’s greatest accomplishments and failures have been determined by our ability or failure to collaborate. As global collaborative systems face unprecedented human-induced changes, there is an urgent call for stewardship akin to the measures demanded by climate change. Beyond humans, it is essential to understand collaborative behaviors across different species, and how properties of an ecosystem affect, and are affected by, the behavior and nervous systems of each animal.
We create software tools for understanding and reasoning about collaborative intelligent systems (see our github repo, collab-creatures. We're developing analysis tools to work on datasets from a broad range of species and environments, collected by our team as well as collaborators. The datasets we collect capture high-resolution movement of groups of animals, as well as 3D geometry of the environment. Our approach involves integrating and synthesizing knowledge across different disciplines, species, and spatiotemporal scales (see our first paper). We aim to produce accessible open-source software tools that empower scientists, policymakers, and the public to make more informed decisions about collaborative intelligent systems.