美国俄克拉荷马大学博士后职位—气象学

美国俄克拉荷马大学博士后职位—气象学

俄克拉荷马大学创建于1890年，是一所位于美国中南部俄克拉何马州的公立研究型大学。

俄克拉荷马大学在2021年度《美国新闻与世界报道》大学排名中位列全美第127名，世界第405位，根据CWUR世界大学排名2021年OU位列世界392名,卡内基高等院校分类标准指定的国家最高等级研究型大学。该校石油工程专业突出，在全美排第4位，是历史上第一个设立石油地质专业的高等院校。拥有全球最大的建于大学内的自然历史博物馆，其下属OUMC为全球顶尖医学院之一，在器官移植、神经外科学等领域均处于国际领先地位。OU会计学以及金融学研究成果优秀，根据杨百翰大学会计学研究排名（BYU Accounting Ranking），2021年该校累计在世界顶级会计学期刊发表论文的数量位列世界50名，学校坐落在俄克拉何马州的诺曼，距离俄克拉荷马城大都会区仅有30分钟的路程。该校毕业多名杰出校友，其中包括NBA球星特雷·杨,格里芬，新浪网CEO 曹国伟；澳门特别行政区原特首崔世安；中国科学院“百人计划”入选者韦昌富；

Postdoctoral Research Associate of Meteorology- Ethical and Responsible AI for Weather and Climate

University of Oklahoma

Job Description

Basic Purpose/Job Function:

The NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES) is an NSF funded AI Institute that brings together universities, government, and private industry to develop trustworthy AI for environmental science. AI2ES will uniquely benefit humanity by developing novel, physically based AI techniques that are demonstrated to be trustworthy, and will directly improve prediction, understanding, and communication of high-impact environmental hazards.  

We are looking for a Postdoctoral Research Associate with expertise in AI/machine learning, Earth System sciences and ethical and responsible conduct of research.  Ensuring that AI is developed and deployed in an ethical and responsible manner is a key part of creating trustworthy AI. The Postdoctoral Research Associate will extend our work on bias categorizations for Earth Science applications and help to create a bias and risk management framework.

This Postdoctoral Research Associate position will be located at the University of Oklahoma in Norman, OK (remote work is possible) and will work with Dr. Amy McGovern, the PI and director of the AI2ES institute and Dr Ann Bostrom (co-lead of risk communication for AI2ES, located at the University of Washington).  The Postdoctoral Research Associate will also collaborate with other institute personnel at the University of Oklahoma in the School of Computer Science and the School of Meteorology as well as with personnel across AI2ES. This collaboration will also be in conjunction with NIST and their AI Safety Institute as well as with other agencies including NOAA.

Position Summary    

The Postdoctoral Research Associate will be a key part of a team that is focused on developing ethical and trustworthy AI for Earth Science applications. The Postdoctoral Research Associate will work with interdisciplinary collaborators across the institute.  At the University of Oklahoma, this will include working with the School of Computer Science and the School of Meteorology as well as colleagues at NOAA and NIST.  This is an exciting position that will allow the Postdoctoral Research Associate to work at the forefront of the development of trustworthy AI and ethical guidelines for the rapidly growing field of AI applied to the Earth Sciences.

Expected job duties

Research in ethical and responsible trustworthy AI and responsible conduct of research (70% time)

The postdoc\'s main duties are to develop and test bias assessment and management strategies and risk management frameworks focused on bias in AI for Earth Science applications.

Knowledge transfer and institute level integration (15% time)

The postdoc is expected to integrate with the other postdocs, students, and researchers throughout the institute.  This may involve travel to other institute sites.  It will also involve video meetings with other sites.  Additionally, the postdoc may be involved in working with students.

Publishing and Sharing results (15% time)

The postdoc is expected to be actively publishing and presenting results at both AI and atmospheric sciences conferences and journals.  This may involve travel.

Required Job Qualifications  

Ph.D. in Machine Learning/Artificial Intelligence and experience with atmospheric sciences or a PhD in Atmospheric Science or closely-related field and experience with Machine Learning/Artificial Intelligence.  Note the PhD does not need to be completed to apply for the position but must be completed prior to beginning the postdoc.

Demonstrated interest and training in ethics and bias assessment/mitigation

Experience with large spatiotemporal data sets

Demonstrated experience of applying at least one machine learning or advanced statistical technique to an atmospheric-science-related problem.

Demonstrated curiosity, creativity, and enthusiasm to learn new skills.

Experience pursuing research, both independently and as part of a research team.

The successful candidate must be legally authorized to work in the United States by the desired start date. AI2ES cannot provide visa sponsorship for this position.

Preferred Job Qualifications  

Experience with large atmospheric science data sets

Experience with neural networks/deep learning

Prior research studying atmospheric variability, extreme weather, or subseasonal-to-seasonal prediction.

Prior knowledge of a broad range of ML topics, as demonstrated by classes taken and/or research performed.

Prior experience collaborating across disciplines.

Experience with tensorflow (preferred) or pytorch

Experience with HPC systems

Experience or interest in working in interdisciplinary teams, including with social scientists

Application Details:

Special Instructions to Applicants -

In your cover letter, please address all of the required and preferred qualifications. A complete application will include a cover letter, resume or CV, and three professional references. References of finalist candidates will be contacted for letters of recommendation; candidates will be notified prior to their references being contacted.

The successful candidate must be legally authorized to work in the United States by the desired start date. AI2ES will not provide visa sponsorship for this position.

If you have a need for hybrid or remote work, please mention that in your cover letter.

This position is for one year, with the option to renew up to 2.5 years total based on performance.

You will submit your application to the link here: http://apply.interfolio.com/151506

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