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machine learning (TML). TML is a cross-disciplinary field that combines machine learning, security/privacy and transparency. As a doctoral researcher your goal is to conduct research in the fast-paced field
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and emerging applications, such as multi-domain autonomy and aerial mobility. With rising risks to PNT systems from interference, spoofing, and cyber-physical attacks, unified, security-aware integrity
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unique research infrastructure and lab facilities to conduct world-leading fundamental and applied research within communication, networks, control systems, AI, sound, cyber security, and robotics
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opportunity to participate and propose other projects within the group and so also develop his/her/their own research agenda. We are working on various topics related to applied ML and cyber-security, including
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improved adaptability and scalability for autonomous vehicles, their black-box nature presents significant challenges for safety assurance. Traditional validation methods rely on pre-defined datasets
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); specialization in digital twins and software engineering of control systems (PhD#3). Experience with swarm robotic systems, manipulator design, cyber-physical systems, SIMULINK, and robotic software development
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infrastructure. However, the increasing application requirements and rising threats from intentional interferences, spoofing, and cyber-physical attacks expose vulnerabilities in conventional GNSS-centric systems
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, this PhD position is for you. At DTU Compute, you will develop novel methods in streaming process mining and artificial intelligence to tackle real-world cyber threats, working side by side with leading
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, Policy & Management The Faculty of TPM provides an important contribution to solving complex technical-social issues, such as energy transition, mobility, digitalisation, water management and (cyber
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Renewable Gas Systems, we develop innovative digital and analytical solutions to enable secure, flexible, and data-driven electricity grids. By combining digitalisation with advanced planning and operational