63 learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" "Inria" Postdoctoral positions in Netherlands
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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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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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successful scaling at an early stage of the innovation process. At the moment, there is a need for a better understanding how to approach responsible scaling. In the project you will learn about how
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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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and Resources. We work on education and research in mechanical engineering, civil engineering and industrial design engineering. Together, we learn by making, creating, and innovating, addressing
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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
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. Our community is open and informal, with more than 7,000 students, 1,000 PhD students, and 1,400 staff members from all over the world. If you would like to learn more about the Faculty of Science and
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for reasoning, on efficient and explainable machine learning for extracting and structuring information from large datasets, and on combining the two in neuro-symbolic AI.