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, copyrighted, or biased. By studying brain data recordings and building computational models that mimic real populations of neurons, the project aims to uncover active unlearning: how the brain learns
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algorithms and complexity theory, including in both well-established settings (e.g., sequential computation on a single machine and distributed/parallel computation on multiple machines) as well as emerging
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Bayesian approach (Lages, 2024). Techniques used: Computational modelling, Bayesian inference, sampling and simulation techniques, prior distributions and posterior predictive checks, model comparison
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Martian meteorite falls using advanced correlative microscopy techniques. To determine if they are the same or different Methods We will use a correlative, big data approach that combines X-ray computed
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: Understand the relationship between POPs and the glacial environment: POPs will be characterised and quantified within different glacial substrates (snow, ice, water, cryoconite, sediments) and within
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Huntington’s disease, and a preclinical model of schizophrenia. In a parallel program of research, we have been exploring epigenetic inheritance via the paternal lineage. We have discovered the transgenerational
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Professorship - Programme Information (PDF, 123 KB) Recently selected Humboldt Professors Our Alexander von Humboldt Professors for AI WANTED: Alexander von Humboldt Professors (female) The Alexander von Humboldt
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elegant, powerful architectures that make a real difference in education and research. Job Scope/Complexity The Enterprise IT Architect typically involves applications and integration of a broad variety of
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at one time. In non-stationary environments on the other hand, the same algorithms cannot be applied as the underlying data distributions change constantly and the same models are not valid. Hence, we need