Annonce

Dear Colleagues,

I forward the advertisement of an upcoming long program on « Image-Based Scientific Machine Learning for Theories of Biological Dynamics Across Scales, » running February 1 – March 12, 2027 and organized by the National Institute for Theory and Mathematics in Biology (NITMB) in Chicago, IL.  The program will bring together physicists, mathematicians, computer scientists, and biologists to accelerate the development of machine learning methods that can « learn the laws » of biology directly from imaging and video data, spanning molecular, cellular, and tissue scales. The program is organized by Margaret Gardel (University of Chicago), Susanne Rafelski (Allen Institute), and Ilya Nemenman (Emory University).

Scholars in Residence: NITMB will select 16–18 scholars to be in residence for the full six weeks of the program. NITMB will cover travel and lodging for the duration of the stay. Participants are expected to participate in long program activities and pursue related research. Applications are accepted on a rolling basis, with candidates evaluated and notified monthly while space remains. Apply to be a Scholar in Residence

Workshops: During the program, there will be three large workshops (4–5 days each, 55–75 participants) will be held as part of the program. All three are now accepting applications:

Image-based Scientific ML for Advancing Theory in Biology

February 1–5, 2027 | Organizers: Aaron Dinner (University of Chicago), Ehssan Nazockdast (Allen Institute), Hervé Turlier (CNRS)

Information-Theoretic Methods for Learning Dynamics

February 8–12, 2027 | Organizers: Kristof Bouchard (Lawrence Berkeley National Laboratory), Ilya Nemenman (Emory University)

Learning Multivariate Spatio-Temporal Dynamical Models

March 1–5, 2027 | Organizers: Wenying Shou (University College London), Margaret Johnson (Johns Hopkins University), Yanlan Mao (University College London), Jianhua Xing (University of Pittsburgh)

Researchers at all career stages working at the intersection of imaging, machine learning, and biological dynamics are encouraged to apply. Please share this opportunity with colleagues and students who may be interested.