PhD Position in Computer Science (University of Luxemburg)

Contract Type: Fixed Term Contract Duration: 36 Month About the FSTM 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. Your role 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 LearningCommunicate research results in international conferences and journals 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 Write a doctoral dissertation in Computer Science Assist in teaching activities Co-supervise Master and/or Bachelor students Your profile Master's degree in Computer Science, Mathematics, Physics, or related field Strong interests in exploring cutting-edge research questions Strong programming skills in Python and deep learning frameworks (PyTorch, TensorFlow) Solid mathematical foundation in topology, differential geometry, or dynamical systems A pro-active approach to achieving research excellence Commitment, team working and a critical mind Excellent written and verbal communication skills in English are mandatory We offer Multilingual and international character. Modern institution with a personal atmosphere. Staff coming from 90 countries. Member of the “University of the Greater Region” (UniGR) A modern and dynamic university. High-quality equipment. Close ties to the business world and to the Luxembourg labour market. A unique urban site with excellent infrastructure A partner for society and industry. Cooperation with European institutions, innovative companies, the Financial Centre and with numerous non-academic partners such as ministries, local governments, associations, NGOs … How to apply Applications should include: Curriculum vitae including your current contact address, work experience, publications (if any) Cover letter and research statement indicating your topics-of-interest and motivation to apply for the position (max 2 page) A short description of your master thesis (max 1 page) Transcript of grades from all university-level courses taken Contact information for 2-3 referees Early application is highly encouraged, as the applications will be processed upon reception. Please apply ONLINE formally through the HR system. Applications by Email will not be considered. All qualified individuals are encouraged to apply. In line with our values, the University of Luxembourg promotes an inclusive culture. We encourage applications from individuals of all backgrounds and are dedicated to upholding equality and respect for our employees and students. General information: Contract Type: Fixed Term Contract 36 Month Work Hours: Full Time 40.0 Hours per Week Location: Campus Belval Internal Title: Doctoral Researcher Job Reference: UOL07063 The yearly gross salary for every PhD at the UL is EUR 40952 (full time). If you apply for this position please say you saw it on Computeroxy

Contract Type:

Fixed Term Contract

Duration:

36 Month

About the FSTM

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.

Your role

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 LearningCommunicate research results in international conferences and journals
- 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
Write a doctoral dissertation in Computer Science
Assist in teaching activities
Co-supervise Master and/or Bachelor students

Your profile

Master's degree in Computer Science, Mathematics, Physics, or related field
Strong interests in exploring cutting-edge research questions
Strong programming skills in Python and deep learning frameworks (PyTorch, TensorFlow)
Solid mathematical foundation in topology, differential geometry, or dynamical systems
A pro-active approach to achieving research excellence
Commitment, team working and a critical mind
Excellent written and verbal communication skills in English are mandatory

We offer

Multilingual and international character. Modern institution with a personal atmosphere. Staff coming from 90 countries. Member of the “University of the Greater Region” (UniGR)
A modern and dynamic university. High-quality equipment. Close ties to the business world and to the Luxembourg labour market. A unique urban site with excellent infrastructure
A partner for society and industry. Cooperation with European institutions, innovative companies, the Financial Centre and with numerous non-academic partners such as ministries, local governments, associations, NGOs …

How to apply

Applications should include:

Curriculum vitae including your current contact address, work experience, publications (if any)
Cover letter and research statement indicating your topics-of-interest and motivation to apply for the position (max 2 page)
A short description of your master thesis (max 1 page)
Transcript of grades from all university-level courses taken
Contact information for 2-3 referees

Early application is highly encouraged, as the applications will be processed upon reception. Please apply ONLINE formally through the HR system. Applications by Email will not be considered.

All qualified individuals are encouraged to apply. In line with our values, the University of Luxembourg promotes an inclusive culture. We encourage applications from individuals of all backgrounds and are dedicated to upholding equality and respect for our employees and students.

General information: