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-efficient machine learning framework that leverages graph grammar to inverse-design polymers with tailored thermal and mechanical properties. By interpreting molecules as graph networks, they will train a
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condition leading to medical discharge following combat related trauma in our military. Learning opportunities include, but are not limited to: exposure to various aspects of pre-clinical research by
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development pertaining to chemical and biological detection. You will gain experience with algorithms, data analysis, Deep Learning, Python, pytorch and /or tensorflow, NLP, genetic algorithm, computer vision
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of military Service members with extremity amputations. Why should I apply? Under the guidance of mentor(s), you will gain hands-on experience to complement your education and support your academic and
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opportunity is with the DCPH-A Toxicology Directorate that provides toxicological assessment of novel military-relevant compounds (MRC) currently under research, development, testing, and evaluation (RDT&E
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parameter space and interdependence of different variables that affect the desired performance. Artificial intelligence and machine learning models have demonstrated the potential to improve the effectiveness
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because of the large parameter space and interdependence of different variables that affect the desired performance. Artificial intelligence and machine learning models have demonstrated the potential
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education and support your academic and professional goals. Along the way, you will engage in activities and research in several areas. These include, but are not limited to, Learn how to communicate data and
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Anderson, Blount, Knox, Loudon, Roane, and Sevier counties the opportunity to learn about foundational computer and computational science skills. Students will be mentored by ORNL research and technical
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for developing advanced monitoring solutions, creating in silico computer models for mimicking human physiology in trauma, and automating ultrasound image interpretation through deep learning model development