The Center for Mathematical Machine Learning and its Applications (CM2LA), affiliated with Mathematics Department at POSTECH, invites outstanding researchers to apply for a Postdoctoral Fellowship position. CM2LA primarily conducts research on the mathematical foundations of machine learning and its interaction with other fields such as PDE, statistics, and geometry. Our center is a Science Research Center (SRC) funded by the Korean National Research Foundation (NRF).

CM2LA is offering highly competitive Postdoctoral Fellowship positions to individuals with well documented interest in all fields of mathematics including but not limited to mathematical machine learning, modeling, and PDE. This position will support independent and collaborative research on one or more of the above topics. We welcome applications from mathematicians of any nationality who are fluent in English.

CM2LA has three research groups that commit to the goal of expanding the boundaries of research in mathematical machine learning and artificial intelligence. Specific goals of the groups are as follows:

• Large-Scale Physics Based AI (Group 1): The group aims to study large-scale physics-based and scientific machine learning to solve complex, real-world challenges, such as applications ranging from PDEs to materials science and climate modeling. All researchers working on artificial intelligence, computational sciences, and scientific machine learning are encouraged to apply.

• Geometry-based Machine Learning Group (Group 2): The group conducts research on theory of optimal transport, gradient based optimization, mathematical models of artificial neural network, topological data analysis in machine learning and their applications to neuroscience and genomics. All researchers working on geometry, PDEs, mathematical data science, and statistics are encouraged to apply.

• Mathematical Modeling and Analysis Group (Group 3): The group aims to study mixed-scale systems such as fluid-kinetic systems, quantum-kinetic systems, and mathematical biology from the perspectives of mathematical analysis and machine learning. All researchers working on fluid dynamics, kinetic theory, dispersive PDEs, and mathematical biology are encouraged to apply.