Postdoctoral Appointee – Machine Learning for X-ray Science

The Scientific Software Engineering & Data Management Group in the X-Ray Science Division (XSD) at the Advanced Photon Source (APS) invites applicants for a postdoctoral position to develop and apply machine learning algorithms and approaches for x-ray science and instruments.

These machine learning methods will be integrated into a shared platform that will lower the barrier to entry to machine learning driven discovery by leveraging advances in machine learning methods across user facilities, thus empowering domain scientists and data scientists to make new scientific discoveries using existing and new data.

This position will be funded under a 3-year Department of Energy award. The selected candidate will work as a part of a multidisciplinary research team comprised of scientists from several National Laboratories.

Find out exactly what skills, experience, and qualifications you will need to succeed in this role before applying below.

The X-ray Science Division (XSD) of Argonne National Laboratory enables world-class research using x-rays by developing cutting edge X-ray instrumentation and techniques, and pursuing research in the physical, chemical, environmental, and materials sciences.

To accomplish this mission, XSD fully operates 43 beamlines and is a partner in the operation of three more beamlines at the Advanced Photon Source (APS).

Today, the APS collects approximately 10 PB of raw experimental data per year from approximately 100 sophisticated instruments performing work in a variety of scientific domains.

Over the coming decade, the APS anticipates this annual data volume will increase by multiple orders-of-magnitude. This is an exciting opportunity to be at the forefront of using machine learning to develop data and computing solutions needed to answer pressing scientific questions that face the nation today.

The successful candidate will perform R&D and other activities to collaboratively develop and apply new machine learning methods and approaches, and a framework to share and distribute machine learning models / data / networks among the user community.

This framework will capture, store, and track contributions, and enable users to search contributions. The candidate will lead the deployment of these tools at the APS to advance discovery in x-ray data analysis.

Questions about this position should be directed to [email protected].

Position Requirements

• To qualify for this position you must have obtained your PhD within the last three years.
• PhD in the physical sciences, computer science or engineering, or a related field.
• Comprehensive programming proficiency, preferably in Python.
• Experience with machine learning methods and frameworks especially applied to physical science problems.
• Experience with x-ray data analysis and / or modeling, such as crystallography, diffraction, or spectroscopy data analysis and / or modeling.
• Skill in written and oral communications.
• Working knowledge of UNIX or Linux.
• Ability to work as part of a team to solve problems of scientific and technological interest to the APS.

Preferred experience :

• Experience with synchrotron light source / x-ray free electron laser experiments.
• Experience using high-performance computing systems and facilities.

Job Family : Postdoctoral Family

Job Profile : Postdoctoral Appointee

Worker Type : Long-Term (Fixed Term)

Time Type : Full time

As an equal employment opportunity and affirmative action employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation.

In support of this commitment, Argonne encourages minorities, women, veterans and individuals with disabilities to apply for employment.

Argonne considers all qualified applicants for employment without regard to age, ancestry, citizenship status, color, disability, gender, gender identity, gender expression, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.

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