The University of Luxembourg aspires to be one of Europe’s most highly regarded universities with a distinctly international and interdisciplinary character. It fosters the cross-fertilisation of research and teaching, is relevant to its country, is known worldwide for its research and teaching in targeted areas, and is establishing itself as an innovative model for contemporary European Higher Education. 

The Faculty of Science, Technology and Medicine (FSTM) 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

The PhD student will be a member of the Department in Computer Science (DCS) within the Faculty of Science, Technology and Medecine (FSTM) at the University of Luxembourg. He/she will work under the supervision of Prof. Dr. Nicolas Guelfi. The successful candidate will join the Messir Group led by Prof. Nicolas Guelfi.

Research Direction

The research problematics addressed by our group include the definition of software engineering methodologies for the benefits of improving the development of software applications based on machine learning. The focus of this PhD project is to perform research on the definition of a methodology supported by a tool for the modeling, simulation and prediction of the resilience of ecosystems. The candidate will perform research in the framework of machine learning and of model-driven software engineering applied to the resilience of environmental ecosystems.

Activities

• Prepare a doctoral thesis
• Disseminate results through scientific publications
• Assist professor in his teaching activities
• Co-supervise Master and/or Bachelor students

Your Profile

• Master’s degree in Information and Computer Sciences
• Experience in Model-Driven Software Engineering and Domain-Specific Languages
• Experience in Machine Learning and strong skills in mathematics
• Experience in the DREF methodology
• Knowledge of specific software (Xtext, Sirius, Git, Eclipse, Latex).
• Good skills in UML and Python.
• Excellent command of the following languages: English, French
• Knowledge in German and Luxembourgish will be considered as advantage
• Commitment, team working and a critical mind

In Short

• Contract Type: Fixed Term Contract 36 Month - extendable up to 48 months if required
• Work Hours: Full Time 40.0 Hours per Week
• Location: Belval
• Employee and student status
• Starting date: September 2021
• Job Reference: UOL03974

Further Information

Applications should be submitted online and include:

• Motivation letter
• Curriculum vitae
• List of publications (if available)
• Copies of diplomas

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.

The University of Luxembourg embraces inclusion and diversity as key values. We are fully committed to removing any discriminatory barrier related to gender, and not only, in recruitment and career progression of our staff.

Here’s what awaits you at the University

• 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. WiFi on campus. Close ties to the business world and to the Luxembourg labour market. A unique urban site with excellent infrastructure.
• A partner for societyand industry. Cooperation with European institutions, innovative companies, the Financial Centre and with numerous non-academic partners such as ministries, local governments, associations, NGOs …

• Find out more about the University
• Addresses, maps & routes to the various sites of the University

Further information

Prof. Dr. Nicolas Guelfi (nicolas.guelfi@uni.lu)