At IBM Research, we are leveraging the power of Artificial Intelligence (AI) to address challenges in climate change and sustainability. We are currently seeking a talented and motivated PhD student to join our Spatiotemporal Modeling team to work on research and development of Urban Extreme Heat models.
You will collaborate closely with a multidisciplinary team of researchers, engineers, and data scientists to develop, test, and enhance the capabilities of our geospatial AI foundation models for Urban Extreme Heat Modeling and Prediction, with the following responsibilities:
Innovation: Contribute to the identification of research directions and methodologies for the application of geospatial foundation models to Urban Extreme Heat and Urban Heat Island modeling and prediction.
Collaboration: Work closely with researchers, stakeholders, and domain experts to understand their needs and provide technical support. Assist in designing experiments and simulations that align with their needs and goals.
Development: Design, implement, and maintain software components for geospatial foundation models and their application to Urban Extreme Heat. Ensure that the developed features are robust, scalable, and efficient.
Integration: Collaborate with software engineers to seamlessly integrate new features into a solution. Follow software engineering best practices to ensure high-quality code and system stability.
Documentation: Create technical documentation, including design specifications, user guides, and API documentation, to facilitate the integration and usage of new components.
Background in artificial intelligence, machine learning, and deep learning.
Proficiency in Python, and experience with relevant libraries and frameworks.
Excellent problem-solving skills and ability to work independently or as part of a team.
Strong communication skills to convey complex technical concepts to both technical and non-technical stakeholders.
Familiarity with software engineering practices, version control, and agile development methodologies.
Experience with relevant libraries and frameworks such as Pytorch and Tensorflow.
Experience with cloud platforms and deployment is a plus.