Postdoctoral position in the project "Quantum topology and deep learning" (NCN Sonata grant)- Topic:
- Application of machine learning to the intersection of low-dimensional topology and quantum field theory
- Keywords: knots, 3-manifolds, quivers, string theory, deep learning
- Research questions: Can algorithm learn to generate a quiver for a given knot? What distinguishes symmetric quivers that correspond to knots from the rest? What is the relationship between quivers and various kinds of knot invariants?
- Requirements:
- Experience in machine learning
- Interest (and preferably experience) in research applications of machine learning
- PhD in computer science or in related area (for example mathematics or physics)
- Tasks:
- Searching the best machine learning algorithms for the tasks from the project
- Acquiring and exploring the data (related to knots, quivers, and 3-manifolds, mostly generated artifically)
- Designing, implementing and training deep learning models
- Documenting and maintaining datasets and the code
- Starting date:
- Oct 1, 2024
- How long:
- 2 years (till Sep 30, 2026)
- Benefits:
- Possibility of ordering the computer of your choice covered by the grant (for up to ~2000 USD)
- Possibility of collaboration with researchers from California Institute of Technology (Caltech), Rutgers University, University of Amsterdam, Uppsala University, LMU Munich, IAS Dublin, Academia Sinica, University of Tokyo, and other institutions
- Possibility of participation in workshops, conferences, schools and other research activities covered by the grant
- No teaching obligations
- Host:
- Piotr Kucharski
- Remarks:
- Applications submitted via AcademicJobsOnline are fully valid, but candidates might be asked to send additional documents required by the university
finished (2023/11/16, finished 2024/06/22)