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applications. A reverse modeling strategy will be used to design an optimum test matrix. This framework will enable modeling and predicting ion-irradiated mechanical properties using reduced test data, thereby
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for classes, in an undergraduate or graduate program in Computer Science, AI, Cybersecurity, Data Science, or a closely related field. Strong programming skills in Python. Knowledge of machine learning or deep
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graduate degree or certification program is required. All graduate students who are awarded assistantships must be enrolled in graduate coursework during the terms of their appointments. Experience: Academic
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Enrolled in an undergraduate or graduate Mathematics or Computer Science program, or related program All students who are awarded assistantships must be enrolled in undergraduate or graduate coursework
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evaluation of high-entropy alloys (HEAs) for accelerator-relevant applications at Fermilab (Fermi Accelerator National Laboratory). This role offers hands-on experience in materials testing, data analysis
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at Army Research Lab facilities as part of the program Benefits: Hands-on experience with a federally funded research project Mentorship from ASU faculty with expertise in cybersecurity and AI Internship
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applications. A reverse modeling strategy will be used to design an optimum test matrix. This framework will enable modeling and predicting ion-irradiated mechanical properties using reduced test data, thereby
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applications. A reverse modeling strategy will be used to design an optimum test matrix. This framework will enable modeling and predicting ion-irradiated mechanical properties using reduced test data, thereby
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an undergraduate or graduate program in Mechanical Engineering, Electrical Engineering, Computer Science, or a related field. Strong interest in experimental methods, numerical modeling, and/or software development