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by beam (e.g. laser light, electrons) and plasma induced processing. LAMP provides full cycle technology development, starting from novel materials, deep understanding of the background physics of beam
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materials synthesis with synchrotron radiation, neutron scattering, spectroscopy and electroanalytical methods. A major research direction is the development of physical methods and models for the description
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the sufficient imaging resolutions (sub-20 nm) measurements become slow, and because dose accumulates with every repeated measurement, radiation damage sets a hard limit on how long a process can be
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-loop drug discovery. The project aims to establish an integrated Design–Make–Test–Analyze (DMTA) platform combining generative AI, ultra-large synthetically accessible chemical spaces, physics-informed
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process? The primary focus will be on algorithm development, followed by their demonstration at synchrotron experiments. You will start working with real already existing experimental datasets and will have
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the sufficient imaging resolutions (sub-20 nm) measurements become slow, and because dose accumulates with every repeated measurement, radiation damage sets a hard limit on how long a process can be
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data manipulation techniques that exploit optimally the underlying hardware and I/O devices, and we enable new discoveries in scientific domains through automating physical database design and
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, computer science, applied mathematics, statistics, physics, engineering, or a closely related field, completed or near completion. Demonstrated expertise in generative AI, such as diffusion models, flow matching
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information science, broadly defined. Experience in topological materials/ topological quantum computation/ quantum geometry/ exposure to tensor network methods or and/or AI/LLM for physics would be a positive addition
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motivated and dynamic candidates with a strong background in early-Universe cosmology and the Standard Model of particle physics. Previous experience with numerical simulations of the early Universe and/or