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is looking for an aspiring PhD candidate to research causal machine learning and uncertainty quantification for Earth Observation time-series. Currently, predictive AI in Earth Sciences relies heavily
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(HIMS), in close collaboration with industrial partner BOR-LYTE and Smart Industry testbeds. This position offers a unique opportunity to combine inorganic chemistry, spectroscopy, machine learning, and
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-physical systems secure and resilient in the presence of uncertainty and cyber-physical attacks? Then you may be our next PhD candidate in resilient and learning-based control of cyber-physical systems
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team at AMOLF, working on fundamental questions on physical self-learning systems as part of the NWO ENW‑M1 project “How do physical learning systems learn?”. The research position is intended to start
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multidisciplinary team of scientists, clinicians, and patients to improve the diagnostics and therapy of patients with tubulopathies. Where to apply Website https://www.academictransfer.com/en/jobs/360181/phd
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optimization methods for run-time network configuration and control. You will design efficient and lightweight learning-based techniques for automated scheduling, network resource allocation, and parameter
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participation of citizens. You will focus on developing adaptive learning systems that enhance the transparency and contestability of AI decisions through personalized, multimodal explanations. Your job AI is
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participants have a high-quality learning experience. You work closely with academic staff, colleagues across the organisation, and external partners. You support the preparation, delivery, and evaluation
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(U.S.E.) as a Lecturer in Economics. You will teach undergraduate and graduate courses in labour economics and microeconomics, supervise BSc and MSc theses, and contribute to high-quality education. We
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Engineering, Medical Image Analysis, Applied Mathematics or a related field Experience with deep learning for image analysis, preferably in medical imaging Experience with generative modelling, ideally