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Field
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, clinical trials, machine learning, optimization, or related methodological areas. Learning and applying deep learning, agentic AI, and other AI methods in assisting research is encouraged. Research
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should meet the following qualifications and possess relevant expertise: A PhD in Physics, Applied Physics, Electrical Engineering, or a closely related field, with a strong experimental research
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Yip and Dr. Nikolay Atanasov (Department of Electrical and Computer Engineering), and will also collaborate with co-investigators at Stanford University, UC Santa Barbara, Vanderbilt University, UNC
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. Experience with developing machine-learning surrogates for structure-property relationship, generative AI models, material representations, machine learning force-fields (especially extensions to spinful
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neurostimulation (TMS, tDCS, etc.), neuroimaging (EEG, MRI, MEG, etc.), or multi-modal neurostimulation-neuroimaging techniques. Machine learning background and/or knowledge. Experience working with individuals with
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communications, orthogonal time frequency space modulation, radar/sonar signal processing, and machine and deep learning. WORK PERFORMING: Individual will perform research generally in the area of statistical
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transfer (CHT) or fluidic manifold optimization Experience with machine learning/AI (PyTorch or TensorFlow), reduced-order modeling (ROM), or data assimilation (DA) Experience with GPU programming (CUDA and
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developing cutting edge analytic tools for studying the genome transformation and genomic activities. 70% - The candidate will be mainly focusing on developing machine learning methods and/or AI algorithms
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analysis, GIS, and large environmental datasets Experience developing predictive or machine learning models for environmental systems Demonstrated record of peer-reviewed publications Experience
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, researchers, and students on foundational machine learning and biologically informed scientific applications. The position is particularly well-suited to candidates eager to apply their technical expertise in