21 software-defined-network-postdoc PhD positions at Utrecht University in Netherlands
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science, environmental modelling, geosciences, or related field with strong quantitative focus; Strong background in machine learning methods such as neural networks and transformers; Knowledge on handling
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will coordinate your work within an (inter)national network of collaborators, while working in an ambitious, motivated, multi-disciplinary team of veterinarians, clinicians, material scientists
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Climate Center network. Your job We seek a PhD researcher to conduct high-quality economic research on water-resilient landscapes, with a focus on the economic assessment of Nature-based Solutions (NbS
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to enroll in the graduate training programme of e.g. the Netherlands Graduate Research School of Science, Technology, and Modern Culture (WTMC), the Sustainability Transitions Research Network (STRN
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project is comprised of 2 PhD positions and 2 postdoc positions which will be filled during the period September 2026 - September 2031. You will join a collaborative research group that also includes PhD
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), the Sustainability Transitions Research Network (STRN) or the European Forum for Studies of Policies for Research and Innovation (EUSPRI). Where to apply Website https://www.academictransfer.com/en/jobs/357988/phd
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collaboration with societal stakeholders, ranging from policy actors (ministries, provinces) and intermediaries (e.g. network organizations). Using interviews, workshops and observations, you will uncover how
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define how AI can support—not replace—human judgment, ensuring that technology empowers rather than undermines trust and autonomy. You will be part of the DECIDE project: a large-scale, NWO-funded research
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to a corpus of geo-analytical scenarios with questions and corresponding workflows; collaborate closely with another PhD candidate (question modelling), a postdoc (GeoQA reasoning engine) and a technical
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sizes and frequencies by: Measuring rock fractures from UAV data using manual and automated mapping approaches (e.g., machine learning, convolutional neural networks). Monitoring physical weathering