Postdoc/Research Associate at NYU in Machine Learning/Scientific Computing for high-dimensional systems

Description

The Center for Data Science (CDS) and the Courant Institute at New York University (NYU) invite applications for a postdoctoral or research associate position to lead scientific machine learning research as part of a new multi-institution international project, M²LInES, The scientific goal of this project is to develop machine learning techniques to improve climate change simulations. M²LInES is composed of more than 30 scientists at NYU, Columbia, MIT, Princeton, GFDL, NCAR and CNRS, including machine learning experts, climate scientists, computer scientists, and climate model developers. 

The successful applicant will integrate the M²LInES NYU team, currently composed of Profs Joan Bruna, Carlos Fernandez-Granda and Laure Zanna, as well as multiple postdoctoral researchers, and graduate students and collaborate closely with M²LInES researchers at other institutions. The successful candidate will contribute by developing independent ideas at the growing interface between Machine Learning and Climate Science. Topics currently under study include closure modeling for high-dimensional systems, uncertainty quantification, inference with massive heterogeneous noisy datasets, interpretability, latent space search, and dimensionality reduction, with the broader context of further integrating Machine Learning algorithms and foundations into Scientific Computing for physics applications (e.g., fluid flows). 

Successful candidates will have the opportunity to lead research in an exciting new area, take advantage of the unique intellectual opportunities afforded by NYU and M²LInES, and gain experience in working with researchers across different fields.

This appointment, available immediately, will be for one year initially, with the possibility of renewal for up to 3 years, based on performance and availability of funding. 

Qualifications

• Completion of a PhD in physics, mathematics, computer science, engineering, statistics, or a related field at the time of the appointment; 

• Background in scientific computing, numerical analysis and machine learning; 

• 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.

Application Instructions

For full consideration, applicants should submit the following via Interfolio 

• a Curriculum Vitae with a list of publications,

• a cover letter (no more than 2 pages) detailing their research experience, how their interests would fit the project, career plans, and available start date,

• 3 letters of recommendation (late letters will be accepted but should be received before interviews).  

Please note the deadline to apply is May 9th 2022. Applications will be accepted until the positions is filled. 

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