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-world problems in collaboration with our industrial partners. Where to apply Website https://phd.fbk.eu/calls/detail/symbolic-model-checking-techniques-for-embedded… Requirements Research
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process data locally while ensuring efficient and scalable artificial intelligence at the edge. TinyML and Edge AI have demonstrated the feasibility of embedding machine learning models on such devices
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research covers a wide range of topics from radiobiology, radiation physics and space research to radiation therapy. The radiobiologic modelling group focuses on developing strategies and mathematical models
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 2 months ago
Offer Description INEGI is opening a call for applications for the recruitment of a PhD Researcher in the field of Scientific Artificial Intelligence, Optimisation and Computational Modelling Applied
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of neural networks, for reconstructing MR images directly from MR signals. By incorporating a physical model of the MR signal into the training of the INR network, we aim to compensate for the effects
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to define glycan features that govern influenza attachment and to develop advanced in vitro receptor models reflecting airway tissues. Your tools: Interdisciplinary receptor analysis: you will work with
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. Equilibrating such films by brute-force molecular dynamics is prohibitively slow. Instead, we build them using diffusion models at coarse-grained resolution, formulated so that the model yields not only realistic
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into an existing computer model for sediment flux through submarine canyons; and validate the model on monitored submarine canyons. The responsibility of the PhD position advertised here will mainly involve
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environment, collaborating with experts in atmospheric chemistry, ecology, air quality, exposure science and environmental modelling. The position offers excellent training opportunities, access to national and
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extension for another 6 months. It is based in the Schulenburg group (https://evoecogen-kiel.de/ ). The PhD project aims at understanding how evolutionary principles can be harnessed to constrain bacterial