Sd-25140 Phd Student-applying Formal Methods and Knowledge Engineering in Ai Regulatory Sandboxes

Description
**Temporary contract | 14+22 (+12 months) | Belval**:
**Are you passionate about research? So are we Come and join us**:
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.

**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.

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**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.

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**SPECIFIC ROLES AND RESPONSIBILITIES RELATED TO THE PHD TOPIC**:
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1. Formalization of Inputs_**
- 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**.

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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.

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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