26 postdoc-in-thermal-network-of-the-physical-building PhD positions at Leiden University
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PhD position understanding and designing nanomedicines-membranes interaction: physics based approach
Apply now The Department of Physics of Leiden University welcomes applications for a fully funded 4-year PhD position understanding and designing nanomedicines-membranes interaction: physics based
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Apply now The Faculty of Science and the Leiden Institute of Advanced Computer Science (LIACS) are looking for a: PhD Candidate in AI for Network Analysis (1.0 FTE, 4 years) About this position
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culture of mutual support and collaboration between researchers. CML has two Departments: Industrial Ecology (CML-IE) and Environmental Biology (CML-EB). Presently, about 150 fte (including postdocs and
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across scales, combining multiple fields including physics, mathematics, astronomy, history & philosophy of science, and social science. Its approach to societal engagement throughout the project’s 5-year
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The successful applicants will work as part of the Groeien met Groen Staal (GGS) programme, which aims to make the Dutch steel sector CO₂-neutral by 2050. Steel is crucial to modern society and plays a significant
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5 Sep 2025 Job Information Organisation/Company Leiden University Research Field Engineering » Materials engineering Engineering » Process engineering Environmental science » Global change
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) Project Why apply? Generative AI and large-language models (LLMs) are about to turn computer-aided engineering into true human–AI co-design. In the new MSCA Doctoral Network GenAIDE we team up with Honda
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transparent and intelligible. Although explainable AI methods can shed some light on the inner workings of black-box machine learning models such as deep neural networks, they have severe drawbacks and
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Leiden, which will maximize access to learning opportunities, local research networks, and access and facilities. Candidate profile: Applications are invited from outstanding graduates who meet the
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workings of black-box machine learning models such as deep neural networks, they have severe drawbacks and limitations. The field of interpretable machine learning aims to fill this gap by developing