PhD Position in Topological Deep Learning (Department of Computer Science)

at  Universit du Luxembourg

The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character.
The Faculty of Science, Technology and Medicine (FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine.
Through its dual mission of teaching and research, the FSTM seeks to generate and disseminate knowledge and train new generations of responsible citizens in order to better understand, explain and advance society and environment we live in.

We are seeking highly motivated candidates with a strong interest or background in one or more of the following areas:

• Deep learning, with a focus on geometric/topological frameworks
• Dynamical systems theory and analysis
• Scientific machine learning and physics-informed neural networks
• Complex systems and network science

The successful candidate will be employed at Doctoral level (PhD) at the Department of Computer Science of the University of Luxembourg (UL). She/he will be supervised by Prof. Dr. Jun Pang and is expected to conduct research on topological deep learning and its interdisciplinary applications.

The candidate’s tasks include the following:

• Conduct excellent research in the field of Topological Deep Learning
• Develop innovative topological deep learning architectures
• Design and implement methods for interdisciplinary applications, such as de novo scientific component design and analysis
• Research novel approaches integrating topology, deep learning, and dynamical systems theory to model and predict complex system behaviors
• Collaborate with research partners on interdisciplinary applications
• Communicate research results in international conferences and journals
• Write a doctoral dissertation in Computer Science
• Assist in teaching activities
• Co-supervise Master and/or Bachelor students