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motivated Neuroscience postdoctoral fellow. In addition to neuroscience research experience, having familiar with machine learning/AI/ big data processing will be an asset. A major part of this PDF
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: Machine learning/deep learning model development for biomolecular data analyses and prediction Research Area: Data science and computational chemistry Required Skills: A Ph.D. in relevant field within
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contribute to the collaborative TQT research community. Principal Investigator: Na Young Kim Project Name: Solid-state analog Optimization Solver and Quantum Machine Learning (Theory) Research Area
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, biochemistry, molecular biology, protein engineering, computer science, artificial intelligence/machine learning, biophysics, nanotechnology, etc., are encouraged to apply.
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: Department of Electrical & Computer Engineering Term: Winter 2026 Course subject code: ECSE 552 Course Title: Deep Learning Course Credits: 4 credits Location: ENGMD 279 Schedule: Monday and Wednesday: 8:35 am
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engineering, manufacturing, and artificial intelligence. Responsibilities: Conduct comprehensive literature reviews on machine learning techniques applicable to design-manufacturing integration. Pre-process
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area: Reinforcement learning; probabilistic approaches in machine learning; mathematical foundations of machine learning. Previous experience as an instructor at the university-level is required. Related
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: Technical Skills in the following categories are necessary: Strong background in image processing techniques. Experience with AI, machine learning and deep learning algorithms, particularly in computer vision
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, harvest and store clinical data and methods used to create predictive models (including but not limited to methods associated with machine learning). Furthermore, issues related to delivery of predictive
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: COMP 550- Course Title: Natural Language Processing Course Number: COMP 551- Course Title: Applied Machine Learning Course Number: COMP 555 - Course Title: Information Privacy Course Number: COMP 558