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exists or an alternative method toward the sophisticated and expensive laboratory instrumentation is being required. The MICROB-PREDICT aims to develop personalised, microbiome-based treatment strategies
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factors in assessing risks to human health. This goal will be accomplished by identifying molecular key event (KE) biomarkers, predictive of chemically induced adverse health effects in humans, that feed
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human values and ethical principles into AI systems? We are researching novel techniques, from preference learning to constitutional AI, to ensure that model behavior remains beneficial and predictable
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characterization, and integration of machine learning to correlate synthesis conditions with functional performance. The goal is to establish predictive synthesis strategies for oxygen vacancy control, with
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liquid phases have been predicted. It can be realized using atoms with two valence electrons, such as ytterbium or strontium. To investigate such SU(N) physics, our group has developed a quantum-gas
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of artificial intelligence (AI) and biomedical engineering. Research directions include deep learning, natural language processing, brain–computer interfaces, and their applications in disease prediction, drug
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and computational methods. For a computational project, for example, this enables generating new datasets, and experimental testing of in silico predictions. The group is funded by European Research
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Research Engineer - Tools developer for LSQUANT platform (Theoretical and Computational Nanoscience)
Waals heterostructures. The project will contribute to the establishement of such a platform and interface with in-house computational artillery of TCN (www.lsquant.org) and to demonstrate predictive