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management platform that connects institutes to facilitate a rapid and efficient exchange among experimental and computational groups Devising an approach in invertible predictive modeling that links
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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
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-modal datasets. It will use advanced analytical models to generate evidence about new and existing inflammatory pathways and how these will impact the progression of dementia. The PhD (DPhil) programme
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water basins across HMA. We will simulate future changes in temperature, precipitation, and snowmelt using climate projection models. These projections, combined with hydrological modelling, will allow us
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machine-learning surrogate models capable of delivering near-DFT (density functional theory) accuracy in just a few CPU seconds per structure. This approach will enable the high-throughput screening of tens
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phenomena such as the spread of misinformation or the formation of filter bubbles. For this, we rely on rigorous probabilistic methods to model and analyse the intrinsic complexities of these systems
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with a wide range of data formats and engaging with data experts and database managers. The second major focus is advanced data analysis and statistical modeling to identify patterns in fish distribution
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“Cellular Plasticity in Myeloid Malignancies: From Mechanisms to Therapies”. In this CRC we will focus on myeloid malignancies as a model to dissect the various molecular mechanisms that enable and regulate
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critical roles of ion channels—particularly the TRP superfamily—in physiological and pathological processes. Our interdisciplinary approach spans from foundational electrophysiology to translational models
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access to additional employee benefits through our Terms of Employment Options Model. In this way, we encourage our employees to continue to invest in their growth. For more information, please visit