We are seeking a highly motivated Postdoctoral Researcher to join our team. The selected candidate will engage in pioneering research to develop the mathematical statistics theory that underpins physics-informed Machine Learning algorithms.
This role presents an opportunity to tackle complex problems at the nexus of deep learning, ordinary and partial differential equations, and mathematical statistics.
The theoretical research is motivated by real data coming from diverse domains such as agriculture, psychotherapy, infectious diseases, medicine, and more.
Key Responsibilities:
* Develop and analyze the theoretical foundations of physics-informed Machine Learning algorithms.
* Collaborate with interdisciplinary teams to apply developed theories and models to real-world scientific and engineering problems.
* Publish research findings in high-impact journals and present work at international conferences.
* Contribute to the development of proposals for research funding.
Benefits:
* Access to cutting-edge research facilities and computational resources.
* The chance to work in a dynamic, interdisciplinary research environment.
* Professional development opportunities, including participation in workshops, seminars, and conferences.
Requirements: * PhD in Statistics, Mathematics, Physics, Computer Science, or a related field, with a solid foundation in mathematical statistics and Machine Learning.
* Proven experience in theoretical research.
* Ability to work independently and as part of a collaborative research team.
This position is open to all candidates.
