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simulation or machine learning of soft and biological matter, e.g. Monte Carlo, molecular dynamics and finite element computer simulations. Origins of life research involving topology and chirality
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PyTorch, TensorFlow, and Scikit-learn. Familiarity with cloud computing and AI frameworks. Extensive experience working on one or more areas: image processing, machine learning, time series, digital health
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ATAC sequencing, spatial transcriptomics, proteomics, whole-genome sequencing, functional screens, bioinformatics, and/or data algorithms including machine learning will be given preference. A successful
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be disseminated through academic publications and online webinars. The successful candidate will have a PhD in human-computer interactions or computer science and related fields, with demonstrable
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single synthetic program of computational geometry. Specific interests include morphology, design topology, discrete differential geometry, packings, and machine learning methods for unstructured geometric
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machine learning for real-world applications. Scalable Digital Health Solutions: Develop methodologies for reliable, long-term motion monitoring across diverse user groups and environments
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-12 units dedicated to global arts and cultures in the Museum’s collections. Design materials that build connections with the New York State Common Core Learning Standards and may incorporate elements
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multimodality imaging including PET/CT, SPECT/CT, and PET/MRI with a focus on integrating machine learning techniques. The appointment will be two years from the date of hire with a possibility of extension
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in the health sciences, including fields such as healthcare informatics, movement and rehabilitation sciences, medical imaging, remote sensing, computer vision, mental health, data fusion
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Experience with high performance computer clusters (e.g, TAMU-HPRC, UT-TACC, NVIDIA Data Center). Preferred Qualifications Background in estuarine ecology, aquatic vegetation Experience with image analysis