Doctoral Researcher in Trustworthy World Models for Critical Systems (University of Luxemburg)

Doctoral Researcher in Trustworthy World Models for Critical Systems Contract Type: Fixed Term Contract Duration: 36 Month About the SnT The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character. The Interdisciplinary Centre for Security, Reliability and Trust (SnT) at the University of Luxembourg is a leading international research and innovation centre in secure, reliable and trustworthy ICT systems and services. We play an instrumental role in Europe by fueling innovation through research partnerships with industry, boosting R&D investments leading to economic growth, and attracting highly qualified talent. We look for researchers from diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and ICT Services & Applications. The successful candidate will join the SERVAL research group and contribute to research and development activities at the intersection of Generative AI, Multimodal Machine Learning, Trustworthy AI, and physics informed learning. The group is developing novel methods, tools, and platforms to support the safe deployment of AI systems in real-world environments. Current research activities focus on topics such as: Constrained Generative models for graph and tabular data. Security, robustness, and trustworthiness of AI systems Evaluation, benchmarking, and testing of AI systems AI systems for healthcare, energy and financial applications Your role The position holder will be required to perform the following tasks/will do research on the following topics: Explore Foundation Machine Learning topics, including designing new architectures, and training algorithms Implement World Models for simulation on multimodal data (vision + tabular, vision + graph) Evaluate the trustworthiness to assess these systems in real-world critical settings. The candidate will contribute to both fundamental research and the development of practical software platforms and demonstrators in collaboration with industrial and public-sector partners. More generally, the PhD thesis is part of a large initiative at Serval and SnT, which aims to support the reliable deployment of AI systems. The subject of the thesis will be “Exploring Trustworthy World Models for multimodal vision-graph-tabular settings”. It will involve exploring methods and approaches for building data-driven and physics-informed World Models for tasks mixing images, text and structured data (graph, tabular), such as simulation of flooding, cancer and power grids. For further information, please contact Dr. Salah Ghamizi: salah.ghamizi@uni.lu Your profile Master in Computer Science. Major in machine learning or computer vision is an asset Strong programming skills Strong analytical skills Industry experience in information and communication technology for energy or healthcare will be considered as an advantage Commitment, team working, a critical mind, and motivation are skills that are more than welcome Knowledge of both the basics and the latest developments of machine learning, in particular foundation models and other generative AI models Academic Eligibility (Thesis): Applicants must demonstrate at least B2-level proficiency in the language of their thesis. For details and accepted certificates, please visit the Application for admission – Doctoral Candidates. We offer A modern, dynamic university with a personal and inclusive atmosphere. Multilingual and international character. Staff coming from more than 90 countries. Member of The Guild of European Research Intensive Universities An exceptional research environment, supported by skilled staff and high-quality equipment. Strong links to professional sectors and the Luxembourg labour market. A unique urban campus with excellent infrastructure A partner for society and industry. Cooperation with European institutions, innovative companies, the Financial Centre and a wide range of non-academic partners including ministries, local governments, associations, and NGOs How to apply Applications should include: Curriculum Vitae Cover letter 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 is committed to achieving gender parity among its staff. Should candidates present equivalent profiles, preference will be given to female candidates in all departments where gender parity is not yet achieved. 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. General information: Contract Type: Fixed Term Contract 36 Month Work Hours: Full Time 40.0 Hours per Week Location: Kirchberg Campus Internal Title: Doctoral Researcher Job Reference: UOL08387 The yearly gross salary for every Doctoral researcher at the UL is EUR 44531 (full time). If you apply for this position please say you saw it on Socioloxy

Doctoral Researcher in Trustworthy World Models for Critical Systems

Contract Type:

Fixed Term Contract

Duration:

36 Month

About the SnT

The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character.

The Interdisciplinary Centre for Security, Reliability and Trust (SnT) at the University of Luxembourg is a leading international research and innovation centre in secure, reliable and trustworthy ICT systems and services.
We play an instrumental role in Europe by fueling innovation through research partnerships with industry, boosting R&D investments leading to economic growth, and attracting highly qualified talent. We look for researchers from diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and ICT Services & Applications.

The successful candidate will join the SERVAL research group and contribute to research and development activities at the intersection of Generative AI, Multimodal Machine Learning, Trustworthy AI, and physics informed learning. The group is developing novel methods, tools, and platforms to support the safe deployment of AI systems in real-world environments. Current research activities focus on topics such as:

Constrained Generative models for graph and tabular data.
Security, robustness, and trustworthiness of AI systems
Evaluation, benchmarking, and testing of AI systems
AI systems for healthcare, energy and financial applications

Your role

The position holder will be required to perform the following tasks/will do research on the following topics:

Explore Foundation Machine Learning topics, including designing new architectures, and training algorithms
Implement World Models for simulation on multimodal data (vision + tabular, vision + graph)
Evaluate the trustworthiness to assess these systems in real-world critical settings.

The candidate will contribute to both fundamental research and the development of practical software platforms and demonstrators in collaboration with industrial and public-sector partners. More generally, the PhD thesis is part of a large initiative at Serval and SnT, which aims to support the reliable deployment of AI systems.

The subject of the thesis will be “Exploring Trustworthy World Models for multimodal vision-graph-tabular settings”. It will involve exploring methods and approaches for building data-driven and physics-informed World Models for tasks mixing images, text and structured data (graph, tabular), such as simulation of flooding, cancer and power grids.

For further information, please contact Dr. Salah Ghamizi: salah.ghamizi@uni.lu

Your profile

Master in Computer Science. Major in machine learning or computer vision is an asset
Strong programming skills
Strong analytical skills
Industry experience in information and communication technology for energy or healthcare will be considered as an advantage
Commitment, team working, a critical mind, and motivation are skills that are more than welcome
Knowledge of both the basics and the latest developments of machine learning, in particular foundation models and other generative AI models

Academic Eligibility (Thesis): Applicants must demonstrate at least B2-level proficiency in the language of their thesis. For details and accepted certificates, please visit the Application for admission – Doctoral Candidates.

We offer

A modern, dynamic university with a personal and inclusive atmosphere. Multilingual and international character. Staff coming from more than 90 countries. Member of The Guild of European Research Intensive Universities
An exceptional research environment, supported by skilled staff and high-quality equipment. Strong links to professional sectors and the Luxembourg labour market. A unique urban campus with excellent infrastructure
A partner for society and industry. Cooperation with European institutions, innovative companies, the Financial Centre and a wide range of non-academic partners including ministries, local governments, associations, and NGOs

How to apply

Applications should include:

Curriculum Vitae
Cover letter

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 is committed to achieving gender parity among its staff. Should candidates present equivalent profiles, preference will be given to female candidates in all departments where gender parity is not yet achieved.

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.

General information: