Launched in 2016, The Flatiron Institute has since grown into a bustling hub for computational science, with hundreds of researchers working on problems in astrophysics, biology, mathematics, neuroscience and quantum physics.
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.
Conformational heterogeneity and probability distributions from single-particle cryo-electron microscopy
Single-particle cryo-electron microscopy (cryo-EM) is a technique that takes projection images of biomolecules frozen at cryogenic temperatures. A major advantage…Current Opinion in Structural Biology
We present an algorithm to solve the dispersive depth-averaged Serre-Green-Naghdi (SGN) equations using patch-based adaptive mesh refinement. These equations require…arXiv:2307.05816
Several recent works have introduced highly compact representations of single-particle Green’s functions in the imaginary time and Matsubara frequency domains,…Physical Review B
Andrew Millis is co-director of the Center for Computational Quantum Physics and associate director for Physics at the Simons Foundation. He has done fundamental research on heavy fermion compounds, quantum phase transitions, ‘colossal’ magnetoresistance materials and high transition-temperature superconductivity.