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Field
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approaches are gaining importance for autonomous vehicles. However, the training and certification of autonomous systems with machine learning components is a huge challenge, since the learned behavior is
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. MATLAB, C/C++, Python. Highly motivated and keen on working in an international and interdisciplinary team. Applicants with strong background in the following fields are preferred: Machine Learning Formal
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research team working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI
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enable students to earn associate or bachelor’s degrees through a combination of in-person, online or blended learning. All of our system institutions place strong emphasis on service — helping to build
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machine learning, modeling algorithms, and/or mapping applications. Applicants must have a PhD in ecology, wildlife sciences or geospatial modeling. Additional Information: Salary Information: Commensurate
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, and robotics. ARG's research interests include topics such as robot learning, human-robot interaction, Generative AI, computer vision, closed-loop control, extended reality (XR), and computational
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). The position offers the opportunity to interact with international collaborators and industry partners. The fellow is expected to explore and design solutions based on machine learning (ML), as well as the use
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&M AgriLife is uniquely positioned to improve lives, environments and the Texas economy through education, research, extension and service. Click here to learn more about how you can be a part of
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-based and machine-learning approaches), the digital twin will provide decision-makers and industry stakeholders with actionable insights about when, where, and how corrosion risk evolves. As a postdoc
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to observe next. By combining Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more efficient, intelligent, and impactful. You will integrate field