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applications towards materials science. Generative machine learning models have emerged as a prominent approach to AI, with impressive performance in many application domains, including materials discovery
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neuropsychological testing. Our group applies sophisticated statistical tools to these unique and state-of-the-art datasets in order to model processes related to aging and disease. The project assistants will join a
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application! Your work assignments We are looking for one PhD student working on generative AI/machine learning, with applications towards materials science. Generative machine learning models have emerged as a
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datasets in order to model processes related to aging and disease. The project assistant will join a growing multidisciplinary team of neuroscientists, statisticians, biologists and engineers, who
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approach combines behavioral experiments, psychophysiology, computational modeling, and brain imaging (fMRI). We offer a dynamic, international research environment where you can contribute to top-level
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Project description The postdoctoral fellow will explore synaptic processes and white matter pathways between remote brain areas in vivo in animal models that underlie plasticity in the prefrontal
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transported in nature and urban systems from an engineering perspective. This includes but is not limited to, comprehension and modeling of the components of the hydrological cycle as well as geospatial
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printing with concrete, and UMA will contribute by exploring new building design with biomass in the 3D printing process. Your primary tasks include: 3D modelling using Rhinoceros 3D and Grasshopper software
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will be expected to dedicate your time develop a high-biofidelity, high-resolution computational rat model with dual applications: i) advance the mechanistic understanding of brain injury by linking
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in mouse models and cell cultures. Analyze and interpret omics data using bioinformatic pipelines in Python and R. Perform experiments in cell culture and animal models to validate the findings