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circular economy practices, aligned with European sustainability, resource efficiency, and Digital Product Passport (DPP) policies. Research background Digital Product Passports are emerging as a cornerstone
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, digital pathology, whole-slide image analysis, self-supervised learning, foundation models, multiple-instance learning or high-performance computing is an advantage. Prior knowledge of pathology or breast
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smart illumination design and advanced AI-driven data processing. Miniaturization of the sensing platform will be achieved through the use of freeform optics, enabling the integration of multiple optical
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your academic CV, including: a transcript of study results. two reference letters with appropriate contact details. an English proficiency certificate. A short research proposal aligned with your
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the "Other Attachments" field, please upload the following documents as separate attachments: 1. Cover Letter (to be uploaded as a separate attachment). If you are applying for multiple projects, please submit
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investigate whether runners can be used as "human sensors" to assess the quality of the infrastructure. You have a Master's degree in Civil Engineering, or a related field that aligns with the topic of the PhD
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to design compact, cost-effective and robust multi-optics imaging systems (i.e. combining multiple optical channels onto a single sensor) which can address these issues. The goal is to create a flexible
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research will address clinically relevant questions that arise directly from patient care, including treatment sequencing, optimization of systemic therapies, management of (high-grade) NEN. In addition, you
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. The candidate will design and analyse an energy consumption model for modern cellular base stations that accounts for contribution of multiple components and functions, such as idle static power, MIMO operation
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scalable, sustainable, and policy-aligned circular design and manufacturing strategies. Education: You do not hold a doctoral degree. You hold a Master’s degree in Mechanical Engineering or a closely related