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Physics Informed Machine Learning method which exploits the advantages of physics-based and data-driven models, while mitigating the disadvantages. This research will contain experimental and modelling
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on text and image feature learning for news ecosystems, analysing the complex multidimensional feature space of visual information to support data-driven journalism. This includes experiments
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will develop a design-build-test-learn cycle, combining high throughput experiments and active learning to obtain synthetic cells with the desired properties. You will integrate liquid handling robots
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of work, careers, leadership, Diversity and Inclusion, meaningful work, well-being, proactivity, HRM, sustainability, learning, community, trust, and innovation. School of Business and Economics We
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models and agents: how to consolidate and transfer physical knowledge into learning systems for the real world. Physics engines and physics-informed neural networks are powerful, but the open problems
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the postdoc will explore familiarity with KEMs and their institutional embedding with policy makers. Needs and preferences for KEM utilization and envisioned learning trajectories within municipalities will be
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, with a strong interest in the integration of geospatial Artificial Intelligence (AI) and machine learning. Are you enthusiastic about the chance to combine research in Remote Sensing and AI with teaching
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to different diseases under abiotic stresses. This postdoc position offers the opportunity to learn the needs for keeping plant breeding innovative in the future, and to apply your research skills in the domain
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; an excellent background in bioengineering, biomaterials, hands-on experience of primary cell culture, organoids, or microfluidic systems; experience on or willing to learn anaerobic microbial cultivation and
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study climate, and an inspiring environment for education and research. Learn more about our codes of conduct We are located on one physical campus, in the heart of Amsterdam's Zuidas business district