一、实验室简介

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。

作为一个多领域的科学和工程研究中心，大约2000多名才华横溢的科学家和工程师云集于此，共同解决关乎人类发展和地球命运的诸多挑战。通过与大学、工厂和其他国家实验室合作，阿贡的科学家们努力探索新途径来推动能源领域的科技创新，小到研究分子级别的新材料，大到探索广袤地球乃至浩瀚宇宙。

二、招聘职位

阿贡国家实验室的能源、环境和经济系统分析中心的高级电网建模小组正在寻找一名专门的博士后研究员。该职位非常适合热衷于推动分布式能源(DER)和可再生能源融入电网的人。被选中的候选人将参与应用最先进的机器学习和深度学习算法，为输配电系统运营商开发网络安全、优化和控制解决方案。

工作职责包括但不限于:

-开发ETL管道，为ML模型训练获取多模态时间序列数据。

-使用CNN、LSTM和Transformer架构开发ML模型，用于异常检测、时间序列预测和代理模型等应用。

-为电网控制问题或网络安全应用设计和实施基于博弈论和强化学习的解决方案。

-进行分布式网格管理和控制的联邦学习应用的研究和开发。

-执行探索性数据分析，并根据电网测量结果生成分析结果。

-与多学科团队合作，为能源系统和电网领域的复杂挑战开发创新的解决方案和技术。

-通过研讨会、期刊文章、技术报告以及在会议和研讨会上介绍研究成果。

-为能源部项目的开源软件开发计划做出贡献。

职位要求

-计算机科学、电子工程、运筹学或相关领域的博士学位。

-数学/统计基础扎实，具有信息物理系统建模和分析经验。

-精通Python。

-熟练使用至少一个ML框架(Keras，TensorFlow或PyTorch)编写脚本。

-能够在团队环境中独立工作和协作。

-具有跨学科研究的经验。

-成熟的解决问题和分析能力。

-较强的口头和书面沟通能力。

-有在高影响力期刊上发表文章的记录和提案开发经验。

-符合阿贡的核心价值观:影响、安全、尊重、诚信和团队合作。‍

优先条件:

-电力传输和分配系统，DER操作，电网建模和模拟的工作知识。

-具有使用强化学习和/或变压器架构开发ML解决方案的经验。

-熟悉AutoML、联邦学习和博弈论建模。

-熟悉电力系统分析软件，如MATPOWER、PSS/E、OpenDSS或类似软件。

The Advanced Grid Modeling group at Argonne National Laboratory's Center for Energy, Environmental, and Economic Systems Analysis is looking for a dedicated Postdoctoral Researcher. This role is ideal for someone passionate about advancing the integration of distributed energy resources (DER) and renewable energy into the power grid. The selected candidate will be involved in applying state of the art machine learning and deep learning algorithms to develop cybersecurity, optimization, and control solutions for transmission and distribution system operators.

Job duties but not limited to:

-Develop ETL pipelines to ingest multi-modal time series data for ML model training.

-Develop ML models using CNN, LSTM, and Transformer architectures for applications like anomaly detection, time series forecasting, and surrogate models.

-Design and implement game theory and reinforcement learning-based solutions for power grid control problems or cybersecurity applications.

-Conduct research and development in federated learning applications for distributed grid management and control.

Perform exploratory data analysis and generate analytics from power grid measurements.

Collaborate with multidisciplinary teams to develop innovative solutions and technologies for complex challenges in energy systems and power grid domains.

-Present research findings through seminars, journal articles, technical reports, and at conferences and workshops.

-Contribute to open-source software development initiatives for Department of Energy projects.

Position Requirements

-Ph.D. in Computer Science, Electrical Engineering, Operations Research, or a related field.

-Solid foundation in mathematics/statistics with experience in cyber-physical systems modeling and analysis.

-Proficiency in Python.

-Proficiency in scripting using at least one ML framework (Keras, TensorFlow, or PyTorch).

-Ability to work both independently and collaboratively in a team environment.

-Demonstrated experience in interdisciplinary research.

-Proven problem-solving and analytical skills.

-Strong communication skills, both oral and written.

-Record of publications in high-impact journals and experience in proposal development.

-Alignment with Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.

Preferred Qualifications:

-Working knowledge of electric power transmission and distribution systems, DER operations, and grid modeling and simulation.

-Experience in developing ML solutions using reinforcement learning and/or Transformer architecture.

-Familiarity with AutoML, federated learning, and game theoretic modeling.

-Familiarity with power system analysis software such as MATPOWER, PSS/E, OpenDSS or similar.

三、申请方式

Email:info@zhengzicn.com