Postdoctoral Researcher

The Scientific Machine Learning (SciML) Research Lab at the university of Haifa excels in merging statistical inference, dynamical systems modeling, and AI to dissect complex phenomena. Our projects span from psychotherapy to environmental sciences, advocating physics-informed machine learning to weave neural networks with dynamic physical processes. As we push the frontiers of theory and practice, we remain committed to pioneering transformative solutions that empower data-driven decision-making and innovation across diverse sectors.

Position Overview:

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.

Qualifications:

• 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.

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.

Application Process:

To apply, please submit the following documents to [email protected]:

• A cover letter detailing your research interests and suitability for the position.
• A comprehensive CV, including publications and relevant work experience.
• Contact information for three references.

For further information about the position or application process, please contact us: [email protected].