Assistant Computational Scientist: Deep Learning for X-ray Science - Military Veterans
at Argonne National Laboratory
The Advanced Photon Source (APS) ( https://www.aps.anl.gov/ ) at Argonne National Laboratory (Chicago, US) invites applicants for a computational scientist staff position to develop deep learning (DL) methods and tools for x-ray science experiments. At the APS, we are developing DL models for accelerated data analysis, experimental steering and scientific knowledge extraction. X-ray characterization provides a powerful means of studying materials at extreme resolution and under operando conditions but require challenging data handling and computational resources. The successful candidate will lead the development of a program leveraging high-performance computing (HPC) and DL training at scale to address these data and compute challenges.
The successful candidate will be responsible for developing algorithms, scientific software and machine learning (ML) workflows for large-scale x-ray data analysis, including large DL models. They will work closely with and participate in data-intensive experiments. They will be responsible for reporting relevant results in publications and talks at conferences and will maintain cognizance of state of the art techniques and methods in ML and x-ray science.
The successful candidate will be part of a cross-lab, highly inter-disciplinary team of experts in ML, applied math, HPC, signal processing, computational physics and x-ray science. The appointee will benefit from access to world-leading experimental and computational resources at Argonne including the world's first exascale computer (Aurora) and one of the brightest synchrotron x-ray sources in the world (APSU).
Candidates with hands-on experience developing and deploying DL models at high data rate materials characterization instruments particularly at synchrotrons and XFELs are encouraged to apply. Candidates are encouraged to include a cover letter in addition to a CV.
- A Doctorate or equivalent education and experience is required.
- Knowledge of x-ray/electron/optical physics, including diffraction, optics, detectors, scattering etc
- Experience with deep learning (DL) libraries such as Tensorflow, PyTorch, JAX etc.
- Publication record in applying ML to X-ray, electron or optical characterization data.
- Experience with ML guided experimental data acquisition.
- Role model Argonne's Core Values.
- Understand, value, and promote diversity.
- Experience in experimental technique development related to x-ray, electron or optical characterization.
- Skill in programming in Python and one other language (C/C++, Go, Rust etc).
- Experience with version control such as Git and collaborative software development.
- Experience with training DL models at scale.
- Skill in written and oral communications. Experience interacting with scientific staff and research groups. Ability to work effectively as a member of a team. Ability to effectively communicate with people of diverse backgrounds and skill sets.
- Experience with computational modeling packages related to x-ray, electron or optical characterization.
Research Development (RD)
Computational Science 2
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