Doctoral Researcher in Machine Learning Security and Testing
Join SnT’s SERVAL group to develop testing frameworks and security principles for trustworthy AI systems.
Best for:Best for computer science graduates with expertise in machine learning, software engineering, or cybersecurity, seeking to contribute to cutting-edge AI security research with industry collaboration.
Overview
This PhD position at the University of Luxembourg’s Interdisciplinary Centre for Security, Reliability and Trust (SnT) focuses on advancing machine learning security and testing. The successful candidate will design technical testing algorithms, define quality and security standards for ML systems, and collaborate with industry partners to build practical evaluation tools. The research aligns with SnT’s mission to ensure reliable AI deployment, bridging fundamental research and real-world applications. The role is based at the SERVAL research group, which specializes in AI agents, trustworthy AI, and cybersecurity. Applicants should have a strong background in computer science, software engineering, or machine learning, with proficiency in programming and analytical problem-solving.
What you'll do
- Develop technical testing algorithms to assess machine learning systems in real-world conditions.
- Define quality and security principles for machine learning systems and platforms.
- Collaborate with industry and public-sector partners to design practical software platforms and demonstrators.
- Conduct fundamental research on AI security, robustness, and trustworthiness.
- Contribute to the development of evaluation and benchmarking tools for AI systems.
- Participate in research activities at the intersection of AI agents, cybersecurity, and software engineering.
Requirements
Degree required: Master of Science in Computer Science or a related field, with a major in machine learning, software engineering, or software security
Degree offered: PhD
Language: English; fluent written and verbal communication skills required. At least B2-level proficiency in the language of the thesis.
Instruction: English
- Strong programming skills (e.g., Python, Java, or similar)
- Proven analytical and problem-solving abilities
- Familiarity with machine learning fundamentals, including large language models and agentic frameworks
- Experience with software engineering practices and tools
- Ability to work collaboratively in interdisciplinary teams
- Applicants must hold a master’s degree in a relevant field.
- No specific nationality or residency restrictions mentioned.
- No age limits or prior experience caps specified.
Funding & terms
How to apply
Applicants must submit their application online through the university’s HR system. Required documents include a CV, cover letter explaining motivation and alignment with the research focus, transcripts of university-level courses, and a research statement (300 words). Early applications are encouraged as the process is rolling.
- CV
- Cover letter
- Transcript of all university-level courses
- Research statement (300 words)
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