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) training personalized computational models in new contexts, and (iii) studying in-silico clinical intervention strategies. The postdoctoral fellow will have the opportunity to: Learn about computational
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following areas: AI and machine learning, natural language processing, large language models (LLM), experience in designing prompts, fine-tuning LLMs, or distributed systems. Good knowledge in one or more of
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‑science and machine‑learning techniques to improve the performance and reliability of existing models, including classification and prioritisation models. Develop, test, and refine analytical approaches and
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skills, including proficiency in statistics, scientific programming, and/or modelling. We especially welcome candidates interested in applying AI and machine learning to analyse heritage datasets
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machine learning (ML) along with data from previously solved problem instances to solve new, yet similar, instances more efficiently than with general purpose algorithms such as Newton`s method. In
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Summary The Residential Mentor will play a vital role in supervising and mentoring high school participants in the BGSU Upward Bound Summer Residential Learning Community. Upward Bound, a federally
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Rock is a metropolitan research university that provides an accessible, quality education through flexible learning and unparalleled internship opportunities. At UA Little Rock, we prepare our more than
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Informatics, Health Data Science, Biostatistics, or a closely related area. Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time-series modeling, or NLP. Demonstrated
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patients requiring urgent or emergent intervention. The fellowship provides comprehensive training in data engineering, exploratory analysis, statistical modeling, machine learning, and artificial
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of department computing assets, including process of requisitions. A successful candidate will be self-motivated, interested in learning and troubleshooting; a team player, hands on and creative; and have the