Post Doctoral Fellow

The postdoctoral researcher will contribute to the development of a novel mathematical and computational framework for hierarchical optimization within graph-based formulations. The position focuses on designing scalable algorithms that integrate multi-level objective structures, combining tools from optimization, graph theory, stochastic modeling, and artificial intelligence. The successful candidate will be responsible for developing prototype solvers, implementing efficient and scalable methods (including GPU-accelerated approaches), and contributing to an open-source software toolbox.

The research will include applications to large-scale datasets, particularly in computational genomics, and will involve both theoretical development and practical validation. The postdoctoral fellow is expected to contribute to high-impact publications and collaborate within an interdisciplinary research environment.

• PhD in Mathematics, Statistics, Applied Mathematics, Computer Science, or a closely related field
• Strong background in one or more of the following: optimization, graph algorithms, stochastic modeling, or machine learning
• Experience with scientific programming (e.g., Python, C++, or similar) and numerical methods
• Familiarity with large-scale data analysis and/or high-performance computing environments
• Ability to conduct independent research and contribute to collaborative projects
• Track record of research output (publications or strong preprints)
• Research experience in one or more of the following areas: hierarchical optimization, graph-based algorithms, probabilistic modeling, or computational statistics
• Experience with scalable algorithms, high-performance computing, or GPU acceleration
• Interest in interdisciplinary applications such as computational genomics or large-scale data science
• Publications in reputable peer-reviewed journals or conferences
• Experience contributing to open-source scientific software