2,522 learning-"https:"-"https:"-"https:"-"https:"-"Computer-Vision-Center" PhD positions
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Primary supervisor Vincent Lee Research area Machine Learning Adversarial Machine Learning (AML) is a technique to fool a machine learning model through malicious input. Due to its significance in
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Primary supervisor Reza Haffari Research area Data Science and Artificial Intelligence Although deep learning has produces state of the art results on many problems, it is a data hungry technology
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unknown relies on the development of complex algorithms at the forefront of statistics, machine learning, and data science. This multi-disciplinary project will combine data science and artificial
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Primary supervisor Hui Cui Research area Cybersecurity This research project aims to address the critical need for privacy-enhancing techniques in machine learning (ML) applications, particularly in
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Primary supervisor Thanh Thi Nguyen Research area Machine Learning Deepfakes, derived from "deep learning" and "fake," involve techniques that merge the face images of a target person with a video of
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: Mastery of Deep Learning fundamentals, including experience with Vision Transformers (ViTs), Generative Models (e.g., Diffusion or VAEs), and Self-Supervised Learning. Information Theory: Familiarity
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partners, and offers voluntary insurance that includes vision, dental, long-term disability and more. Retirement Options: Attractive options include Florida Retirement System Pension Plan, State
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Adaptive Learning in Brain-Robot Interactions School of Electrical and Electronic Engineering PhD Research Project Self Funded Dr Mahnaz Arvaneh Application Deadline: Applications accepted all year
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doctoral researcher(PhD student) with a strong background in probabilistic machine learning, statistics, applied mathematics, computer science, or a related field, and strong programming skills, to
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required. Federal Work Study Award required. You must be available to work during Fall 2026. Job Description: The College of Pharmacy Office of Lifelong Learning is hiring a part-time Federal Work Study