By studying the changing geometry of cell nuclei in fruit flies, scientists have gained a better understanding of the dynamics that shape the part of our cells that carries our DNA.
It develops, deploys and maintains computational infrastructure — from supercomputers to desktop PCs — dedicated solely to the use of Flatiron researchers.
Machine learning has become an indispensable tool for computational science, and it is an active and growing area of research throughout the Flatiron Institute.
Underlying all biological processes are molecules and their interactions with each other. However, our ability to understand how these molecules function over biologically relevant scales remains very limited.
This collaboration, directed by Greg Bryan of Columbia University, aims to understand and determine the evolution and initial conditions of our universe, using observations via a Bayesian forward modeling approach.
- | Columbia University
- | Lawrence Berkeley National Lab
- | Harvard University
- | Stockholm University
- | Institute D'Astrophysique de Paris
- | Université de Montreal
- | Princeton University
- | Carnegie Mellon University
- | Max-Planck Institute for Astrophysics
For distinguished scientists with a particular interest in diversity and inclusion
Scholars may engage in a variety of activities, such as working on scientific projects, starting new collaborations, mentoring junior scientists, and organizing or participating in workshops and career development events.
A major effort of the Flatiron Institute is the development and support of high-quality, open-source software for research.
We report an accurate and efficient classical simulation of a kicked Ising quantum system on the heavy hexagon lattice. A…PRX Quantum
Recent developments in Markov chain Monte Carlo (MCMC) algorithms allow us to run thousands of chains in parallel almost as…arXiv:2110.13017
Decomposing imaginary time Feynman diagrams using separable basis functions: Anderson impurity model strong coupling expansion
We present a deterministic algorithm for the efficient evaluation of imaginary time diagrams based on the recently introduced discrete Lehmann…arXiv:2307.08566