Dear Colleague,

The Neuro-AI and Geometric Data Analysis Lab at NYU and the Flatiron Institute (PI: SueYeon Chung) are seeking postdoc fellows, grad students, and a full-time research assistant. We have multiple positions with flexible start dates.

Our lab is situated in the heart of downtown New York City, operating jointly at NYU's Center for Neural Science and the Center for Computational Neuroscience at the Flatiron Institute, a research institution within the Simons Foundation dedicated to computation. The lab is also affiliated with NYU’s Center for Data Science, Minds, Brains and Machines Initiative, CILVR group, and Cognition & Perception Program.  

Our current research focuses on the following areas, all geared towards analytically and phenomenologically connecting various levels of abstraction and scales in information processing within biological and artificial neural networks:

1. Neural Manifolds as a Population Coding Theory:

• Objective: Develop a normative theory of neural population geometry, known as neural manifolds.

• Aim: Create an analytical theory that establishes a connection between the structure in high-dimensional neural data and the underlying computational processes.

• Toolkits: Statistical physics (replica method, random matrix theory), neural network and machine learning (theory & applications), convex optimization, high-dimensional geometry, and statistics.

• Applicants with backgrounds in statistical physics, machine learning theory, and theoretical neuroscience are strongly encouraged to apply.

2. Neural Manifolds as a Data Analysis Method:

• Objective: Employ the theory and methods of neural manifolds for neural data analysis.

• Background: We welcome applicants with backgrounds in neuroscience and cognitive science who are familiar with a wide range of experimental datasets and various model systems. An interest in collaborating with cutting-edge theory subgroups is highly encouraged.

3. Deep Learning Theory for Neuroscience:

• Objective: Apply analytical insights from deep learning theory to computational approaches in systems neuroscience.

• Toolkits: Statistical physics (replica method, random matrix theory), machine learning theory and applications, high-dimensional statistics.

Additionally, we are exploring frontier topics, including the theory of neural manifolds for causality in large language models, the theory of multitasking and cognitive control, and the theory of semantic/categorical hierarchies and knowledge representations.

If you are passionate about advancing our understanding of the structure of information in neural systems, we encourage you to apply. We welcome theorists and computational neuroscientists across a range of backgrounds, such as machine learning (applications and theory), theoretical/statistical physics, computer science, computational and systems neuroscience, and cognitive science. 

Join us in this exciting journey at the intersection of neuroscience, computation, and theory. If interested, reach out with a CV, a brief description of research interests, and a list of references to sueyeon@nyu.edu or schung@flatironinstitute.org. (Please note that official job announcements from the Flatiron Institute will be widely circulated in the upcoming weeks, but we are scheduling candidate seminars on a rolling basis.)

Best,

SueYeon Chung 

Assistant Professor

Center for Neural Science, New York University

Center for Computational Neuroscience, Flatiron Institute, Simons Foundation

Personal Website: https://sites.google.com/site/sueyeonchung/