美国阿贡国家实验室博士后职位---实验设计的数值优化

美国阿贡国家实验室博士后职位---实验设计的数值优化

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 Associate in Numerical Optimization for Design of Experiments

Argonne National Laboratory

Job Description

The Laboratory for Applied mathematics, Numerical software, and Statistics (LANS) and the Mathematics and Computer Science (MCS) Division at Argonne National Laboratory invite applicants for a postdoctoral position in the area of numerical optimization. You will investigate and develop new algorithms for solving dynamic optimization problems with applications to sequential design of experiments, motivated by problems arising from various imaging techniques.

Candidates interested in advancing developments in numerical optimization (especially dynamic optimization), active learning, and design of experiments for challenging large-scale data analysis, with interesting and real applications, will find a stimulating environment at the laboratory. Candidates should have been or soon will be awarded a doctoral degree in applied mathematics, statistics, computer science, machine learning or related areas. Knowledge is desired in one or more of mathematical optimization, inverse problems, scientific computing, and/or machine learning.

Position Requirements

Required skills:

·         A completed or soon-to-be completed PhD (typically within the last 0-3 years) in mathematics or a related discipline (e.g. computer science, engineering).

·         Experience in mathematical optimization, machine/deep learning, and scientific computing.

·         Effective verbal and written communication skills.

·         Ability to model Argonne's core values of impact, safety, respect, integrity, and teamwork.

Desirable knowledge and skills:

·         Software development practices for large-scale computation and data-intensive problems.

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