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Job Description
We are looking for postdoctoral research fellows to work on topics on machine learning and its scientific applications. The successful candidate will work with Dr Li Qianxiao on these topics.
The main responsibility of the position include conducting cutting-edge research in machine learning theory and algorithm research, with applications to materials and quantum sciences. Topics include (but are not limited to) data-driven multiscale modelling, approximation and optimisation theory of new architectures and applications in dynamics, and learning and controlling deterministic and stochastic dynamics.
Qualifications
- PhD in (Applied) Mathematics, Computer Science, Physics or related fields
- Minimum two years and above of working experience
- Strong knowledge in machine learning and deep learning
- Familiarity with basic theory of dynamical systems, ODEs, PDEs, and stochastic processes
- Knowledge in computational physics and chemistry are desirable
- Ability to code in Python with at least one of the popular deep learning framework, e.g., Jax, PyTorch, etc
- Academic research with strong publication record. The candidate should have published in top machine learning conferences and journals, such as ICML, ICLR, NeurIPS, JMLR, etc. Publications in applied sciences are also desirable.
