Doktorand inom optimal transport för maskininlärning
Core
Developing and analyzing mathematical models and algorithms connecting partial differential equations, infinite-dimensional optimization, and statistical machine learning to build a theoretical and computational foundation for new methods in statistical inference and generative models.
Role type
PhD researcher in applied mathematics and computational mathematics (machine learning)
Builds
Theoretical and computational foundations for statistical inference and generative models
Domain
Mathematics, Machine Learning, Optimization
Deliverable
research
Required skills
advanced mathematics, applied mathematics, analysis, partial differential equations, optimization, mathematical reasoning, programming (Python)
Preferred skills
optimal transport, stochastic analysis, generative models theory, statistical inference, sampling, numerical simulation
Technologies
Python
Responsibilities
Develop and analyze mathematical models and algorithms; apply optimal transport and gradient flows to machine learning and optimization problems; collaborate with researchers in a dynamic research environment
Seniority
PhD candidate (early career researcher)
