Topic: Development of AI-driven semantic analysis and data evaluation methodologies for autonomous systems and digital twin ecosystems. We seek one experienced member for our team, a Computer Scientist or Engineer, with a strong background in semantic mapping, machine learning, and robotic systems, to contribute to the research and development activities of the ORDEAL and COLOURS projects. The ORDEAL project will organise a technological challenge environment and test procedure for participants to demonstrate accurate positioning in non-permissive environments for single and swarm drones. It involves conducting field test campaigns in diverse environments (including indoor, outdoor, and GNSS-denied settings) and data-based tests to refine positioning algorithms. The project aims to strengthen defence capabilities by building expertise in test planning, evaluation, and counter-UAV measures, fostering innovation and establishing robust testing frameworks for autonomous operations. The COLOURS project aims to drive the digital transition of cultural heritage conservation by developing integrated colour analysis and restoration tools tailored to diverse artefacts, such as polychrome sculptures, textiles, and historic films. Fully interoperable with the European Collaborative Cloud for Cultural Heritage (ECCCH), these tools will provide high-precision analysis, simulate restoration results with perceptual accuracy, and support interdisciplinary collaboration through hybrid spaces. The project also focuses on enhancing public engagement via mixed reality technologies to showcase restored colours and convey artifact authenticity. The candidate will be responsible for developing methodologies that bridge autonomous system capabilities with semantic data interpretation and digital twin technologies. Specifically: • For the ORDEAL project, the candidate will contribute to the organization of technological challenges for autonomous drone navigation. Responsibilities include defining comprehensive guidelines and standards for data annotation, developing evaluation metrics for data-based tests, and managing the quality control of sensor data collected from field tests in non-permissive environments (e.g., GNSS denied). • For the COLOURS project, the candidate will work on the adaptation and semantic enrichment of Heritage Digital Twins (HDTs). This involves developing AI-based tools for predictive analysis and simulation of conservation processes, ensuring the semantic alignment of data models with the ECCCH (European Collaborative Cloud for Cultural Heritage) ecosystem, and supporting the creation of a Colour Knowledge Repository