Urban Systems Lab Research Fellow in Machine Learning

The Urban Systems Lab (USL) at The New School invites applicants for

a Machine Learning engineer as a Research Fellow to assist with development of our machine learning approach for ClimateIQ, an AI-driven climate risk hazard assessment tool funded by Google.org. This role will focus on troubleshooting and optimizing our machine learning model for deployment on the Google Cloud Platform (GCP). As the position is expected to work on time critical deliverables, we anticipate that the start date will be in April 2025 and may be renewed or extended annually, based on external funding.

The Urban Systems Lab is an interdisciplinary research, design and practice space at The New School that provides knowledge and analysis for developing more equitable, resilient, and sustainable cities. The Lab’s work advances cutting edge science, data visualization, and computation to develop systemic solutions to social and environmental challenges driving inequity and injustice in urban areas. We bring together designers, urban ecologists, scientists, researchers and policymakers with the goal to improve the lives of those most vulnerable, and to enhance decision making and science communication from local to global scales. To learn more visit: http://urbansystemslab.com.

Please note: The Urban Systems Lab will transition from The New School to New York University over the summer 2025. This position will focus on time-sensitive deliverables at The New School through June 30, 2025, with the possibility of renewal at NYU, depending on performance and hiring requirements.

The New School, a private university in New York City’s Greenwich Village, serves undergraduate and graduate students across a range of fields with a commitment to bringing practices in design and social research to studying issues of our time and challenging students to become engaged citizens dedicated to solving problems and contributing to the public good. 

The New School is strongly committed to diversity and inclusion in the workplace and particularly seeks applications from members of underrepresented groups, as well as candidates who share this commitment.

POSITION OVERVIEW

The ClimateIQ team at the USL is seeking a highly skilled and experienced Research Fellow in Machine Learning to assist in the development of ClimateIQ, an innovative AI-driven climate risk tool. This position will focus on advancing the technical development of machine learning models and supporting software engineering needs with a particular emphasis on deploying and optimizing ML models on the Google Cloud Platform (GCP). The role will also support the slicing, interpolation and disaggregation of multiple global climate input data, and support optimization of a new model to efficiently downscale datasets. The ideal candidate will have strong Python programming skills, experience with geospatial data processing, working with cross-functional teams, and a strong understanding of machine learning (ML) workflows. This is an exciting opportunity to contribute to a groundbreaking project at the intersection of machine learning, cloud computing, and climate resilience, while gaining hands-on experience with cutting-edge technology and real-world impact. This position will report to the Director of the Urban Systems Lab. 

RESPONSIBILITIES

• Assist with the technical development of the Machine Learning models for ClimateIQ and integration with Google Cloud Platform.

• Troubleshoot and refine Machine Learning models to ensure model performance and accuracy.

• Develop and optimize Python/C++ scripts for geospatial data transformation (e.g., NetCDF/geogrid binaries/GRIB to NumPy/Geotiff and vice versa).

• Implement efficient spatial interpolation methods to downscale climate data (e.g. FNL, ERA5, CMIP6) to 500m resolution.

• Ensure seamless data integration into ML data pipelines, working closely with ML researchers.

• Optimize cloud and HPC-based processing pipelines for large-scale data handling.

• Enable seamless integration and deployment of ML models on Google Cloud Platform.

• Collaborate with interdisciplinary team members to align technical solutions with project goals.

MINIMUM QUALIFICATIONS

• Master’s degree in a relevant field such as Data Science, Computer Science, Statistics, or a related discipline.

• Extensive experience in Machine Learning, including applications of ConvLSTM and CNN.

• Proficiency in working with geospatial data.

• Strong understanding of machine learning data pipelines and preprocessing techniques.

• Experience with cloud-based platforms (e.g., Google Cloud Platform) for scalable machine learning and data processing.

• Advanced proficiency in Python, including libraries such as NumPy, Pandas, and Xarray, as well as deep learning frameworks like TensorFlow/Keras.

• Excellent communication and organizational skills, with the ability to effectively convey complex technical concepts to both technical and non-technical audiences.

PREFERRED QUALIFICATIONS

• PhD degree.

• Experience handling large-scale datasets (e.g., NetCDF, HDF5).

• Familiarity with climate and urban datasets.

• Familiarity with C++ and Fortran.

• Familiarity with R and relevant data science libraries, with experience in cloud-based environments and scalable data processing for model deployment.

WORK MODALITY

In-Person – Employees in this position are required to work on-site at The New School campus, with some remote work flexibility. #LI-ONSITE

SALARY RANGE

$65,000 - $75,000 annually

TO APPLY

Review of applications will be on a rolling basis and open until filled.

Interested candidates should submit: 

• a cover letter describing the applicant’s work experience relevant to this position

• a curriculum vitae

• names and contact information of three references

• example publications, GitHub repo, or other relevant outputs or products

We look forward to receiving your application!