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will be based at the Maritime and Transport Technology department of the TU Delft, working within an interdisciplinary team led by Dr. Roy de Winter. The team includes PhD candidates focusing on digital
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to participation, equity, and inclusion. The European Accessibility Act (EAA), which mandates that EU service providers ensure digital accessibility by 2025, marks a critical step toward reducing these inequalities
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have an interest in pavement engineering and digital infrastructure, join us in shaping the future of smart pavement engineering! You will do your PhD in the Pavement Engineering section, where your
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. Experience in data analysis using Python. The outcomes of this project will be disseminated to the scientific community and a general audience through presentations at (inter)national conferences and through
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education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers
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design. Their research contributes to the theory and practice of sensory and cognitive communication and augmentation design and the section is unique for its cross-disciplinary approach to design for real
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analytical skills, initiative, inventiveness, curiosity, diligence, and critical thinking. Ability to work with scientific and industrial partners. Strong communication skills (high English proficiency), team
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qualifications: MSc university degree in physics, mechanical engineering, or mathematics. High motivation for teamwork and good communication skills. Experience in nonlinear dynamics and /or random vibrations and
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models and LLM-based explanations. You will provide the community with the first tool for self-healing software that is useful for research, education, and industrial use. Your research will be published
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technology and human organisations. You’ll engage with an interdisciplinary community that bridges engineering, behavioural science and policy to co-design approaches for safer, more robust systems