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for interacting with colleagues and stakeholders. Department Specifics: Develop various machine learning and data mining models including convolutional neural networks (CNNs), Transformers, large language models
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difficult and the creation of more intelligent process control strategies and innovative methods of tracking reliability can be achieved with expert informed machine learning techniques, which offer more
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decision-making for complex infrastructure systems. This position offers an opportunity to contribute to interdisciplinary research at the intersection of civil engineering, machine learning, and systems
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for interacting with colleagues and stakeholders. Department Specifics Develop various machine learning and data mining models including convolutional neural networks (CNNs), Transformers, large language models
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physics, applied mathematics, machine learning, bioinformatics, biophysics, spectroscopy, image processing, ecological modeling, molecular biology, plant physiology, marine biology or an interest in gaining
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computer applications used for data recording, analysis, and reporting. Physical Demands and Working Conditions Physical Activities Working Conditions Additional Information Remote Work: A hybrid remote work
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to pregnancy), sexual orientation, gender identity, gender expression, ethnicity or national origin, religion, age, genetic information, disability, or veteran or military status by any member of the KSU
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materials used in defense applications. Online monitoring of additive manufacturing requires a deep understanding of process conditions and microstructural evolution, which can be modeled through machine
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machine learning methods in the context of biological systems Experience with programming (e.g., Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will be hosted
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specified timelines. Ability to prioritize tasks, set milestones, and monitor progress. Continuous Learning and improvement: Willingness to stay updated with the latest advancements in road condition research