Postdoctoral Research Scientist (ML Applied to Neutron and x-ray Diffraction and Spectroscopy)
 Columbia University | |
$65,000-70,000
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 United States, New York, New York | |
| 500 West 120th Street (Show on map) | |
 Jan 26, 2025 | |
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Columbia University in the City of New York: Data Science Institute Location Data Science Institute Open Date Oct 03, 2023 Salary Range or Pay Grade $65,000-70,000 Description PI: Simon Billinge This position is open both to materials scientists with computing experience and Data scientists with an interest in materials science and physics The Data Science Institute (DSI) at Columbia University invites applications for the position of a Postdoctoral Research Scientist focused on the application of machine learning applied to neutron diffraction and spectroscopy techniques in the group of Professor Billinge, funded by US Department of Energy, Office of Science, Office of Basic Energy Sciences. DSI strives to be a force for change. We advance the state-of-the-art in data science; transform all fields, professions, and sectors through the application of data science; and ensure the responsible use of data to benefit society. Drawing on Columbia's strengths in computer science, statistics, and industrial engineering and operations research, DSI was launched in 2012 to unite our expertise and a University-wide interest in this revolutionary approach. The University is a trailblazer in the field and is uniquely poised to expand data science to every corner of the institution. We train the next generation of data scientists, develop innovative technology, foster collaborations in advancing techniques to interpret data and address pressing societal problems, and work closely with industry to bring promising ideas to market. PROGRAM DETAILS The goals of the project are to use data analytic (AI, ML) methods to to leverage the very high dimensional input parameter space of a time of flight neutron experiment to separate signals from different components of a sample. There is growing need to understand the structure-property relationship of materials in real operating devices. Experiments to do this, operando measurements, involve running a real operating device in an x-ray or neutron beam and tracking changing signals as the device operates. A huge challenge in this regard is to separate signals from different components of the device that are in the beam, some of which are interesting and others not. This project aims to use AI/ML to do the signal separation in an automated way. The project will involve developing novel data analytic (AI, ML) algorithms, and implementing them in software (primarily Python), to extract component signals from large datasets of complex overlapped signals. KEY RESPONSIBILITIES The successful candidate will work closely with scientists on the team and will be expected to be involved with data acquisition at experiments, but principally with data reduction and analysis, and be expected to work effectively in a team environment with the combined University and National Laboratory context. Experimental expertise is not a prerequisite, but experience with data analysis and AI/ML is preferred. Qualifications MINIMUM QUALIFICATIONS:
PREFERRED QUALIFICATIONS:
We encourage applications from candidates with diverse backgrounds, experiences,and identities. Application Instructions Applications will be considered on a rolling basis with applicants encouraged to apply as early as possible and preferably before October 31, 2023, with a preferred start date as soon as possible after that. Applicants should apply on-line via the link below and upload the following:
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