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
suey...@nyu.edu or sch...@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/
Lab Website: http://www.sychunglab.org/
