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, Educational Technology, Learning Sciences, Human-Computer Interaction, or a related discipline. Strong interest in research relating to Generative AI, AI literacy, digital learning, workplace learning, or human
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, and sub-daily to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning (ML), to help solve complex agricultural
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interacts with the human body—making it a key factor in both product performance and consumer satisfaction. You will also apply statistical and machine-learning tools to explore how cotton fiber properties
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that are deeply grounded in stochastic analysis and show also development of computational methods towards machine learning. The projects will focus on applications to risk-sensitive decision making and control
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of a scientific mentor, you will gain experience and learn to conduct rigorous data analysis and cross-project data synthesis, identify critical technical and data gaps, and determine future applied
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are built on a foundation of continuous learning and growth . We actively support professional development by providing all full-time staff employees with at least 80 hours of paid time per year and provide
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and processes; - Experience with machine learning and AI tools. Modes of Work Positions that are eligible for hybrid or mobile/remote work mode are at the discretion of the hiring department. Work
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of multi-modal datasets, including MRI/fMRI, behavioural and audio data Develop reproducible statistical and machine learning analysis pipelines Support grant writing, ethics submissions and project
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Python and/or C++. Demonstrable experience with machine learning frameworks (e.g., PyTorch, TensorFlow). Hands-on experience with game AI agents and/or GUI agents such as Mineflayer, Unity ML-Agents
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, Electrical Engineering, or a related discipline. Strong research background in one or more of: Computer Vision Machine Learning Deep Learning Video Understanding Multimodal AI Excellent programming skills in