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modeling with AI/machine learning frameworks is a plus Department Unit/Website: www.ameslab.gov Proposed Start Date: October 1, 2025 Proposed End Date or Length of Term: September 30, 2026 Number of Months
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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that the candidate has prior research experience in one or more of the following research topics: Free space optical communication Visible light communication DSP for coherent optical communication Machine learning
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is connected to the vibrant local ecosystem for data science, machine learning and computational biology in Heidelberg (including ELLIS Life Heidelberg and the AI Health Innovation Cluster ). Your
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the disparities. While foundation models offer great promise for creating more robust machine learning models for a wide array of tasks, it remains an open problem how to foresee their biases across that wide array
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, machine learning, geographical information sciences, and many other topics to help frame and solve the above problems on a national and global scale. The successful candidate will contribute to cutting-edge
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position in statistics, machine learning, and data science. The postdoctoral researcher will be mentored by Professor Yiyuan She and will contribute to the development of innovative statistical methods and
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understanding of artificial intelligence applications and methodologies, such as working knowledge of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI
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Job Description Required Qualifications: (as evidenced by an attached resume) Doctoral Degree (or foreign equivalent) in Computer Sciences, Engineering, Neuroscience, Psychology or a closely related
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of original machine-learning based algorithms and models for multi-modal ultrasound guidance that are intuitive for a non-specialist to use while scanning and trustworthy. You will work with clinical domain