美国阿贡国家实验室2023年招聘博士后职位（AI/ML用于高能X射线衍射显微镜）

美国阿贡国家实验室2023年招聘博士后职位（AI/ML用于高能X射线衍射显微镜）

美国阿贡国家实验室（Argonne National Laboratory，简称ANL）是美国政府最早建立的国家实验室，也是美国最大的科学与工程研究实验室之一――在美国中西部为最大。阿贡前身是芝加哥大学的冶金实验室 (Metallurgical Lab)，现在隶属于美国能源部和芝加哥大学。诺贝尔物理学奖得主费米于1942年在此领导小组建立了人类第一台可控核反应堆（芝加哥一号堆，Chicago Pile-1)，完成了曼哈顿计划的重要一环，并且使人类从此迈入原子能时代。

Postdoctoral Appointee - AI/ML for High-Energy X-Ray Diffraction Microscopy

Argonne National Laboratory

Job Description

The X-ray Science (XSD) division at Argonne National Laboratory invites applications for postdoctoral researchers position for a project to develop artificial intelligence (AI) and machine learning (ML) methods to enhance high-energy X-ray diffraction microscopy at the Advanced Photon Source. The extreme volume and velocity of information associated with this non-destructive microstructure mapping technique can benefit from AI/ML at each stage of data flow, from the sensor to the data center. Because such tools can run at high speeds, thanks to advances in AI streaming inference accelerators, it becomes feasible to extract salient information from in-flight data, in real time, and thus both enabling fast feedback and reducing downstream computational burden. The successful candidate will conduct cutting-edge research in data science and deep learning and apply it to scientific problems, particularly in the materials science and engineering fields. The candidate will play a key role in developing physics-aware AI/ML models, developing workflow building blocks and implement high-speed training on data center AI systems (e.g., Cerebras CS-1 ML accelerator and Argonne's Aurora exascale supercomputer), end-to-end model training workflows and explore AI accelerators for simulation applications.

Position Requirements

Basic Qualifications:

・Ph.D. in material sciences and engineering or related field obtained within the last three years.

・Experience with X-ray science techniques (e.g., tomography, diffraction, etc.).

・Software development practices and techniques for computational and data-intensive science problems.

・Comprehensive experience programming in one or more programming languages, such as C, C++, and Python.

・Ability to provide project leadership.

・Exceptional communication skills, ability to communicate effectively with internal and external collaborators and ability to work in team environment.

・Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.

・Understand, value, and promote diversity.

Preferred Qualifications:

・Experience with machine learning methods and deep learning frameworks.

・Experience on applied machine learning (e.g., successful projects that used ML to resolve scientific problems).

・Experience and skills in interdisciplinary research involving computer and material scientists.

・Experience with high-performance computing and/or scientific workflow.