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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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) Validate the model by performing simulations using conventional methods like nonlinear FEM, and comparing the results to computational observations. 3) Support the educational activities of the Pl through
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of Civil and Environmental Engineering / Chaney Lab: Perform the core of the proposed research activities including processing the remotely sensed LST to compute the spatial statistics, run the HydroBlocks
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Assessment Models (IAMs) such as GCAM or PAGE. The candidate must have a PhD degree in a related field, be fluent in computer programming, preferably python, and will ideally have experience in working with
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, evolutionary biology, computer science, physics, applied mathematics, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control
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Stimulation We seek a highly-motivated individual to conduct research on computational modeling of cortical neuron activation by transcranial electric and magnetic stimulation (TES and TMS). We have an NIH
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independent research activities under the guidance of a faculty mentor in preparation for a full time academic or research career. Conduct research on computational modeling of cortical neuron activation by
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Minimum Qualifications The candidate should have a Ph.D. in chemical engineering, mechanical engineering, chemistry, physics, or materials science with a computational modeling focus. Experience 0+ years
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, and to interact regularly with Dr. Jonathan Campbell to design and execute experimental studies involving animal and cell-based models of metabolic disease. In addition, will also perform the following
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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