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Field
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This position is a good fit for you if you recognise yourself in the following: You hold a PhD in one these fields: computer science, information science, industrial ecology, operational research, environmental
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migration), economy (e.g., trade, businesses and resource flows), technology (e.g., infrastructure mapping, urban plans/construction), as well as potential sources of risks and benefits, including pollution
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2 Oct 2025 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Engineering » Civil engineering Researcher Profile Recognised Researcher (R2) Country
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This position is a good fit for you if you recognise yourself in the following: You hold a PhD in one these fields: computer science, information science, industrial ecology, operational research, environmental
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scalable, flexible, and sustainable building production. You will play a key role in connecting geometry, engineering, and manufacturing logics into a unified computational pipeline. BUILTWISE is an ongoing
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. Qualifications We are looking for a candidate with A PhD in Computer Science, Operations Research, Applied Mathematics, Mathematics, Engineering, or a related discipline. Strong programming experience, for example
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-derived organoid models. You will work closely with in-house technology platforms, including the Single Cell Genomics Facility, Big Data Core and High Throughput Screening Facility. Our research is embedded
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or engineering. Preference will be given to applications submitted by candidates within five years of receiving their PhD. Additional requirements In addition to your CV and your motivation letter, please prepare
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Essentials PhD (completed or near completion) in Computer Science, Computer Vision, NLP, Machine Learning, Computer Graphics/Animation, HCI, or a related field. Strong background in deep generative
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Essentials PhD (completed or near completion) in Computer Science, Computer Vision, NLP, Machine Learning, Computer Graphics/Animation, HCI, or a related field. Strong background in deep generative