Lead Research Scientist - AI for Cyber-Physical Risk at FM in Luxembourg

Job description

Established nearly two centuries ago, FM is a leading mutual insurance company whose capital, scientific research capability and engineering expertise are solely dedicated to property risk management and the resilience of its policyholder-owners. These owners, who share the belief that the majority of property loss is preventable, represent many of the world’s largest organizations, including one of every four Fortune 500 companies. They work with FM to better understand the hazards that can impact their business continuity to make cost-effective risk management decisions, combining property loss prevention with insurance protection. FM is hiring a Lead Research Scientist to build the team's AI and machine learning capability across Cyber Security, Robotics and Digital Twins research. The role answers one question for FM: how do AI-enabled industrial systems create new paths to physical damage and business interruption, and how can AI help us measure that risk? The role has two sides. First, study AI as a source of risk : models embedded in OT/ICS, autonomous robots and digital twins that can fail, drift or be attacked. Second, use AI as a research tool : foundation-level modelling, data pipelines and analytics that turn lab and loss data into risk insight for underwriting and engineering. Across all three domains Set and own the AI research roadmap for the Automation and Intelligent Systems research group. Build shared AI foundations: data pipelines, feature stores, model training and evaluation environments, and MLOps practices that can be reused by all three domains. Develop an AI risk taxonomy for industrial settings: failure modes, adversarial threats, model drift, over-reliance and loss of human oversight, each mapped to physical damage and business interruption outcomes. Design experiments in the lab that make AI failure consequences measurable, not just theoretical. Turn findings into underwriting guidance, risk engineering recommendations, loss-prevention data sheets and publications. Track relevant standards and regulation (EU AI Act, ISO/IEC 42001, ISO/IEC 23894, NIST AI RMF, IEC 62443, ISO 10218) and assess what they mean for insured risk. Mentor researchers and engineers in the team and raise overall AI literacy. Cyber security (OT/ICS) Research adversarial ML against AI used in industrial control: evasion, data poisoning, model extraction and supply-chain compromise of models. Assess AI-based anomaly and intrusion detection for OT networks, including false-negative risk and attacker evasion. Study how LLM agents and AI copilots in engineering and operations tools widen the attack surface (prompt injection, tool misuse, unsafe autonomous actions). Model cyber-to-kinetic scenarios and aggregation exposure where one AI model or vendor is deployed across many insured sites. Robotics and autonomous systems (Physical AI) Conduct cutting-edge applied research on AI on robotic systems — AGVs, AMRs, quadruped and humanoid robots — including embodied AI, 3D mapping and localization (e.g. SLAM, S-Graphs), machine learning, and autonomy for robotic technologies operating in industrial environments. Advance image processing, computer vision, and sensor data fusion techniques, linking them to potential failures while characterizing the associated hazards. Assess vision-language-action and other foundation models for robotics, and the risk introduce. Support robotic experimental approaches, testing and safety work with AI-specific test methods . Digital twins Build physics-informed and hybrid ML models (surrogates, reduced-order models) of industrial processes and equipment. Use digital twins to simulate AI failures and safely, and to generate synthetic data for rare loss events. Assess the fidelity, validation and trustworthiness of twins that operators use for decisions, including twin–asset desynchronisation risk. Explore twins as risk engineering tools: what-if analysis, scenario generation and loss estimation. Machine lear