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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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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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chair group and collaborate with researchers developing innovative approaches for microbiome and genome mining. The research is expected to deliver fundamental insights into the evolutionary history
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at the atomic scale, using density functional theory-accurate machine-learned potentials and molecular dynamics simulations, in close collaboration with leading European research institutes and steel industry
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project focused on developing an advanced machine learning framework for spatio-temporal datasets. The position is for 2.5 years and is partially funded by the Dutch Research Council (NWO) through
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pharmacy. In close collaboration with partners from industry, healthcare, and society, we contribute to the urgent challenges of our time, such as energy, sustainability, digitization, and medical technology
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learning skills; curiosity about societal issues and how AI can be employed for the benefit of the public; strong teamwork skills, as collaboration with other researchers and stakeholders is key
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regularly with your supervisors and other team members, and also be encouraged to develop and explore your own ideas. Furthermore, you will collaborate closely with local and international colleagues working
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to compare rodent behaviour with the human work packages. Collaborating closely with experts in ultrasound imaging, computational modelling, social neuroscience, and rodent circuit neuroscience. Preparing
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independent research while working effectively in a collaborative environment Experience with vision-language models, large-scale representation learning, or foundation model adaptation is particularly