Faculty Position in AI for Observational and Data-Driven Astrophysics

Position Overview

Contract type: Fixed-term, renewable. 3rd contract is open-ended

About the Department of Physics

The Department of Physics is part of the School of Mathematics and Physics (SMP) at Xi’an Jiaotong-Liverpool University (XJTLU). Research in the Department seeks to explore and explain fundamental aspects of the world we live in, from the smallest subatomic particles to the Universe as a whole. Building on its existing strengths, the Department is expanding into application-oriented areas that can drive technological innovation and help address major global challenges. These include AI for Science, quantum technologies, advanced imaging, and materials-related research.

Position introduction

The Department of Physics at Xi’an Jiaotong-Liverpool University (XJTLU) invites applications for a faculty position in AI for Observational and Data-Driven Astrophysics. We seek a highly motivated and well-qualified candidate with a strong background in observational astrophysics and data-intensive research. Research interests may include (but are not limited to) interstellar medium, star formation, exoplanets, time-domain astronomy, and large-scale astronomical surveys.

Responsibilities

• Conduct high-quality research and publish in top journals;
• Actively apply for national and international research funding;
• Supervise research projects of undergraduate and PhD students;
• Deliver undergraduate and graduate courses in physics and mathematics in English;
• Actively participate in departmental and university affairs, and promote interdisciplinary collaboration, particularly with data science and artificial intelligence.

Essential Qualifications/Experience

All applicants must have: A PhD in Physics, Astronomy, or a closely related discipline; Postdoctoral research experience or equivalent academic experience; Demonstrated ability or strong potential in undergraduate and postgraduate teaching; Solid background in observational, experimental, or data-intensive astrophysics; Fluency in written and spoken English.