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environmental conditions. The PhD position is funded through the Norwegian Nuclear Research Centre (NNRC). Link: https://www.nnrc.uio.no/english/index.html The PhD candidate will be part of a dynamic and
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involve collaboration in the nationwide Immunostart-kids network and is embedded within the Maastricht-Nijmegen Translational Research Alliance for Innovative Vaccine Research, ensuring valuable
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-supervised by Prof. Nicolas W. Jager at TU Berlin. For more information on the project, see https://sustainability-governance.net/2026/06/29/erc-advanced-grant-gap… . Location Leuphana University Lüneburg is a
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Realities • Research Theme D: Adaptive and Distributed AI for Intelligent Realities • Research Theme E: Networking for Intelligent Realities This PhD research focuses on developing adaptive and distributed AI
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TensorFlow or PyTorch Knowledge of neural networks – especially convolutional neural networks (CNNs) Understanding of image processing techniques – including filtering, edge detection, and image segmentation
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) network in Delft. A Dual Career Programme is available, to support your accompanying partner with their job search in the Netherlands. Application procedure Applicants should send their CV, a cover letter
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, offers information on their website to help you prepare your relocation. In addition, Coming to Delft Service organises events to help you settle in the Netherlands, and expand your (social) network in
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participate in our international networks. Experience our atmosphere of tolerance and respect. Work-life balance & flexibility A family-friendly working environment (e.g. additional childcare days) Flexible
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(‘Christmas bonus’), regular salary increases in line with collective agreements and additional public sector pension provision (VBL) A secure, interesting and varied role at a cosmopolitan university 30 days
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-order modelling, surrogate models, and machine-learning methods such as neural networks. Control design for flexible reactor operation. Develop advanced control strategies that enable safe and efficient