[EOI] Postdoctoral Associate in Machine Learning and Climate Modeling | New York University - Military Veterans

The Courant Institute of Mathematical Sciences at New York University (NYU) is seeking expressions of interest from postdoctoral researchers to work with Prof. Sara Shamekh at the intersection of machine learning, atmospheric processes, and climate modeling. This position is funded by the Department of Energy (DOE) through the UNSHADE project, aimed at advancing our understanding of shallow clouds and their transition to deep convection, with the ultimate goal of improving their representation in climate models. The successful candidate will leverage cutting-edge machine learning techniques to analyze observational data, addressing key questions around shallow cloud formation and the role of the atmospheric boundary layer. This work will involve developing models that illuminate the processes driving shallow cloud formation, growth, and transitions to deep convection. Key Responsibilities: Design and implement advanced machine learning algorithms, including generative models, for processing observational data. Develop and refine ML-based models that capture the dynamics of cloud formation and atmospheric processes. Collaborate closely with interdisciplinary teams and contribute to project goals, publications, and presentations. This full-time position is ideally set to begin in March. The initial appointment is for one year, with the potential for renewal for an additional year based on performance and funding availability. We welcome and strongly encourage applications from individuals of all backgrounds, particularly those from historically underrepresented communities, including but not limited to women, racial and ethnic minorities, LGBTQ individuals, people with disabilities, and first-generation students. In compliance with NYC?s Pay Transparency Act, the annual base salary range for this position is $62,500 - $78,000. New York University considers factors such as (but not limited to) the specific grant funding and the terms of the research grant when extending an offer. Completion of a PhD in physics, mathematics, computer science, engineering, statistics, or a related field at the time of the appointment; Strong programming experience; Strong interest in the application of machine learning to science and engineering problems; A record of relevant publications in the peer-reviewed scientific literature appropriate to their career stage; Ability to work independently and as part of an interdisciplinary team. For full consideration, applicants should submit via Interfolio, the following by December 15th , 2024: a Curriculum Vitae with a list of publications, a cover letter (no more than 2 pages) detailing your research experience, available start date, career plans, and how your interests would fit the project. 3 confidential letters of recommendation. For additional information, please contact Sara Shamekh (ss18284@nyu.edu) For people in the EU, click here for information on your privacy rights under GDPR: www.nyu.edu/it/gdpr NYU is an Equal Opportunity Employer and is committed to a policy of equal treatment and opportunity in every aspect of its recruitment and hiring process without regard to age, alienage, caregiver status, childbirth, citizenship status, color, creed, disability, domestic violence victim status, ethnicity, familial status, gender and/or gender identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive health decision making, sex, sexual orientation, unemployment status, veteran status, or any other legally protected basis. Women, racial and ethnic minorities, persons of minority sexual orientation or gender identity, individuals with disabilities, and veterans are encouraged to apply for vacant positions at all levels. Sustainability Statement NYU aims to be among the greenest urban campuses in the country and carbon neutral by 2040. Learn more atnyu.edu/sustainability