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implement computational models and perform deep learning analyses for the early detection of Alzheimer’s disease using MRI, biomarkers, and advanced computational techniques · Collaborate with a
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computational and data analytical methodology development and implementation; experience in supervised and unsupervised machine learning, low-dimensional models or deep learning models, and willingness to learn
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circuits that regulate motivational and emotional states. The lab uses many state-of-the-art techniques, including deep-brain calcium imaging (2-photon in vivo microscopy) with single-cell resolution and
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models using a wide variety of data, including clinical, wearable devices, neuroimaging, and –omics data from electronic health record, registry, and research initiatives; and (2) develop and apply novel
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activity. The applicant will also gain exposure to other projects in the lab, spanning from modeling to translational clinical studies and including deep brain stimulation, spinal cord stimulation
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and dynamic wave propagation, in particular: (i) developing domain decomposition methods, (ii) damage models, (iii) nonlinear mechanics. 2) Validate the model by performing simulations using
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surface water dynamics using satellite and field data. This research may also include working with NASA’s new Surface Water Ocean Topography (SWOT) satellite, as well as the opportunity to conduct fieldwork
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data, identifying structural errors in the dataset, and for maintaining a record of all steps from data extraction to dataset assembly · Fitting of machine learning models · Development of instrumental
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pregnancy and early childhood. The project integrates exposure modeling, biomonitoring, and immune profiling to assess early-life susceptibility and long-term health impacts. The Scholar will work closely
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model over the Contiguous United States, and evaluate model deficiencies and model improvements to improve the modeling of spatial heterogeneity of LST in land surface models. In Addition, Will Also