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-cell analysis platform that combines microfluidics, advanced imaging and AI-based analysis to study gut microbial consortia. You will drive the development of the next-generation image analysis and
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automates building and modifying surface structures, submitting DFT calculations, post-processing electronic structure and vacancy energies, and extracting machine-learning descriptors for modeling oxygen
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molecular simulations, and cutting-edge AI techniques including graph neural networks (GNNs) and large language models (LLMs) to accelerate experimental design and discovery of novel materials. The research
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areas of innovation in the renewable energy sector, with a particular focus on emerging technological paradigms and scientific models that support their implementation in energy systems. The activities
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direct support to NASA’s Space Radiation Program. The selected candidate will conduct research focused on developing computer models to simulate the interaction of high charge (Z) and energy (HZE) ions
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cost is essential for a sustainable energy and materials system. Cost reduction will come from cheaper electricity, innovation, market growth, scaling, standardisation, and supply chain development
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struggles to keep up with the growing demand, in MINDnet will investigate neuromorphic computing as a promising solution to support such a demand by getting inspired by the brain’s powerful and energy
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energy phenomena is manyfold, from theory and simulations to their observation from space and ground, and including the modelling and interpretation of their multi-wavelength observations. In this context
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and moved by laser-based optical tweezers in a closed action–perception loop. Your work will help transform current observation-only live-cell imaging microscopy into actively controllable, automated
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. The project involves large-scale analysis of high-dimensional datasets, including: single-cell and single-nucleus RNA sequencing spatial transcriptomics (e.g., 10x Genomics, Xenium) germline and somatic