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Field
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and implements dynamic models, including machine learning algorithms, to optimize student-centered outcomes. Creates clear and actionable visualizations and dashboards using tools such as Power BI
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data input, files/database structures, data transformation, algorithms, and data output by using appropriate computer language/tools to provide technical solutions for highly complex application
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& AI hardware or brain-inspired AI algorithm development, spatial analysis of multi-omics data. We are particularly interested in applicants with a demonstrated track record of translating discoveries
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analysis, and algorithms of computational research questions at a level sufficient to converse with Princeton's world-class researchers to support the ongoing work. This will consist of independent research
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discovery with a strong emphasis on domain-driven impact. Develop, optimize, and transition algorithm prototypes to robust implementations Work with ORNL researchers, as well as internal and external project
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innovative ideas to enhance existing algorithms and frameworks. Analyse and interpret findings, ensuring scientific rigour and practical relevance. Prepare and submit manuscripts to leading conferences and
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of fairness, safety, and privacy in digital technologies. Key Responsibilities: Conduct research and development in trust technologies – translating algorithms into tools and frameworks and implementing working
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in developing new tools to understand the nervous system and to explore theories behind neural phenomena. As for developing new tools, we have been working on network alignment algorithms [FCC+21] and
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assistance systems Collaboration in the development of AI algorithms (LLM, fine-tuning, RAG, AI agents, embeddings) Literature research on the topic of AI What you bring to the table Studies in
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technical subjects such as programming, data science, machine learning, and algorithmic fairness is highly desirable. Candidates must have teaching experience in a degree-granting program, including lecture