SD-25140 PHD STUDENT-APPLYING FORMAL METHODS AND KNOWLEDGE ENGINEERING IN AI REGULATORY SANDBOXES

The Luxembourg Institute of Science and Technology (LIST) is a Research and Technology Organization (RTO) active in the fields of materials, environment and IT. By transforming scientific knowledge into technologies, smart data and tools, LIST empowers citizens in their choices, public authorities in their decisions and businesses in their strategies.

Do you want to know more about LIST? Check our website: https://www.list.lu/

Your LIST benefits

• An organization with a passion for impact and strong RDI partnerships in Luxembourg and Europe that works on responsible and independent research projects
• Sustainable by design, empowering our belief that we play an essential role in paving the way to a green society
• Innovative infrastructures and exceptional labs occupying more than 5,000 square metres, including innovations in all that we do
• An environment encouraging curiosity, innovation and entrepreneurship in all areas
• Personalized learning programme to foster our staff’s soft and technical skills
• Multicultural and international work environment with more than 50 nationalities represented in our workforce
• Diverse and inclusive work environment empowering our people to fulfill their personal and professional ambitions
• Gender-friendly environment with multiple actions to attract, develop and retain women in science
• 32 days’ paid annual leave, 11 public holidays, 13-month salary, statutory health insurance
• Flexible working hours, home working policy and access to lunch vouchers

Temporary contract | 14+22 (+12 months) | BelvalAre you passionate about research? So are we! Come and join us

SUMMARY

As part of Luxembourg’s AI Factory initiative, a fully funded PhD position is open to design and implement a formal and computational framework for structuring the inputs and outputs of AI regulatory sandboxes under the EU AI Act. The position focuses on formalizing legal, technical, and organizational information through mathematical modeling, and on structuring sandbox results into an evolving, linked knowledge base for reuse and transparency.

This is an entry-level scientist working under close supervision. The candidate will perform the job according to plans and directions set up with the thesis supervisor in alignment with the . Organizes own day-to-day activities. Can explain theories, facts, and practices related to own work. Develops knowledge of discipline, RDI methodologies, technical instruments, and protocols. Gains knowledge of LIST RDI activity, it’s partnership ecosystem and the how it responds to market needs.

GENERAL ROLES AND RESPONSIBILITIES

The primarily responsibility of a PhD students is to define their research project and successfully complete it, achieving agreed working plan and objectives in due time. To do so, and depending on their area of activity, their duties may include:

• Discusses and agrees with the supervisory team the hypotheses, experiments, expected results, work plan, and training plan of their research project.
• Respects codes of research ethics and research integrity.
• Keeps track of progress / delays on work plan and related milestones / deliverables. Informs regularly on work progress and addresses promptly any unexpected problem that could impact the project implementation.
• Asks for feedback and scientific discussion, responds positively to advice and guidance from their supervisors, actively seeks collaboration within their RDI team.
• Develops an increasing level of independence in the conduct of their PhD project.
• Demonstrates the ability to generate RDI results under supervision: identifies, defines, and addresses problems in their scientific field; performs comprehensive literature review; conducts experiments; makes analysis, evaluation, and synthesis of information; and elaborates conclusions that can serve as input for the definition of RDI concepts.
• Presents the outcome of their RDI work in the form of scientific papers, posters, presentations in conferences, etc. Prepares a sound thesis manuscript aimed at PhD defence.
• Makes an appropriate dissemination of own research results: participates in forums, seminars, or scientific events.
• Participates in the valorisation and transfer of RDI results. Learns to define patents, licences, or prototypes.
• Completes their training programme according to the partner university’s requirements (e.g., ECTS, publications).
• Takes responsibility for their own skills and career development.
• Respects and applies LIST-HSE rules. Alerts of potential dangers or risks. Takes care of their own safety and that of others.

SPECIFIC ROLES AND RESPONSIBILITIES RELATED TO THE PHD TOPIC

The successful candidate will develop the foundations and tools to support three interconnected pillars of regulatory sandboxing under the EU AI Act: (i) formalization of inputs, (ii) development of assessment tools for trustworthy AI, (iii) structuring outputs into a Knowledge base

1. Formalization of Inputs

§ Model unstructured input information—including legal texts, application forms, onboarding narratives, and risk documentation—using rigorous mathematical formalisms such as graph-based structures, domain-specific languages, or state machines.

§ Translate these models into actionable orchestrations of AI agents or tools capable of parsing, extracting, and transforming these heterogeneous inputs into a computationally operable format. This enables traceability, semantic alignment, and automated reasoning, supported by embedded quality controls.

§ The objective is to ensure that the onboarding and compliance preparation phases of sandboxing are grounded in a mathematically precise and interoperable input framework.

2. Development of Assessment Tools for Trustworthy AI

§ Formalise, from first principles, the core requirements of Trustworthy AI—as defined in the EU AI Act and related ethical and technical literature—into testable and computable properties. These include:
– Fairness: Detection of bias, inequality in treatment, or systemic discrimination across demographic groups.
– Agentic Behaviour: Evaluation of alignment with human goals, autonomy in task execution, and ethical safeguards in decision-making.
– Robustness: Assessment of the AI system’s resilience under input variation, adversarial conditions, or data drift.

§ Based on these formal representations, develop a library of automated, reusable assessment tools capable of running during sandbox engagements. These tools will allow for technical evaluation, structured feedback, and the monitoring of improvements across iterations.

§ This component directly supports the core testing phase of regulatory sandboxing, bridging the gap between abstract legal requirements and concrete technical audits.

3. Structuring Outputs into a Knowledge Base

§ Translate sandbox results—including evaluator feedback, test outcomes, compliance justifications, and final reports—into structured, ontologically grounded representations.

§ Contribute to building a linked knowledge base that captures sandbox experiences over time, enabling benchmarking, reuse of testing artifacts, and horizontal policy learning.

§ Design mechanisms for iterative refinement, where outputs from past engagements inform both future assessments and the evolution of the modeling language itself.

§ The final outcome is a dynamic, queryable infrastructure that serves regulators, auditors, and developers—helping them navigate, compare, and analyse diverse sandbox cases through shared vocabularies and structured evidence.

This knowledge base will serve as a living resource for regulators, developers, and auditors, enabling navigation and analysis across multiple sandbox cases through shared vocabularies and structured evidence.

REQUIRED QUALIFICATIONS

• Master’s degree in the field required for the specific position.
• Solid foundation in mathematical modeling (e.g. graph theory, automata, logic, algebraic structures).
• Strong programming proficiency in Python.
• Experience with ontology engineering or knowledge graph construction is a strong plus.
• Familiarity with NLP or document analysis tools is desirable.
• Good communication and presentation skills.
• Excellent written and spoken English; French is a strong advantage.
• Strong capacity for abstraction, reasoning, and structured problem-solving.
• Demonstrated ability to collaborate across diverse, multilingual teams and translate between technical and organizational contexts.
• Strong communication skills, especially in explaining formal structures and processes to varied audiences.