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sits at the heart of AI@HHMI. Our ambition is big: to create one of the world’s most comprehensive, multimodal 3D/4D microscopy datasets and use it to power a vision foundation model capable
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well as computational simulations based on 3D vertex models. We are now seeking motivated candidates for the position of Project Assistant Professor who have a strong interest in this field. There are no restrictions
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? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description “Development of an AI-driven generative 3D modeling framework". The main
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to: i) lead the design and creation of an experimental setup to examine fluid-structure interactions between 3D-printed models of deep sea sponges and ii) lead the formulation and analysis of finite
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plumes. Atmospheric pollution plumes are inherently three-dimensional (3D) structures, characterised by sharp spatial gradients, rapid temporal evolution, and intricate links between chemical
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clearly and follow research procedures. Preferred Requirements (Highly Desirable) Technical Skills Experience with: 3D imaging models (3D CNNs, UNet variants, VNet) Explainable AI tools (Grad-CAM, LIME
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highly motivated PhD student to develop advanced fracture models for predicting material degradation and failure in additively manufactured steel in nuclear reactor water environments. The project focuses
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intervention systems; digital healthcare systems; real-time physiological monitoring technologies; multimodal data integration and analysis for imaging and sensing. Human Centered Models and Systems: human stem
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to include, but are not limited to: Culture of human induced pluripotent stem cells (iPSCs) and iPSC-derived neuronal models, including 2D neurons and 3D brain organoids Functional and molecular
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obligatory requirements: Advanced knowledge of 3D modeling and experience in areas relevant to the project. Availability is valued The proof of enrolment must be made until the contract signing date, and the