Shahar Kovalsky

The Department of Mathematics at the University of North Carolina at Chapel Hill is recruiting a postdoctoral researcher to work in geometric optimization and scientific computing , with links to machine learning and data analysis . The appointment is within a research environment that combines optimization, geometry, computation, and learning, offering the chance to contribute to methods and algorithms at the interface of mathematics and modern data-driven problems. The successful scholar will work with the Principal Investigator on projects spanning optimization for machine learning, geometric methods for data analysis, and efficient algorithms for learning and inverse problems. Projects are described as being developed jointly, allowing the postdoctoral researcher to shape directions based on their expertise while also pursuing independent ideas. The role includes close research mentorship, regular meetings, support for professional development, and opportunities to collaborate with faculty and students across related disciplines within and beyond the Department of Mathematics. The position also includes teaching and participation in the department’s applied mathematics community, making it a good fit for someone who wants to combine research in computational and theoretical methods with engagement in an active departmental setting.