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at the undergraduate level, or a Ph.D. in STEM Education or a related field focused on undergraduate teaching and learning with content knowledge in Mathematics, Astronomy, Biology, Chemistry, or Physics. Job Specific
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: Computational Linguistics, Language Acquisition, Cognitive Modeling, Machine Learning The Department of Linguistics at the University of Michigan invites applications for a one-year Postdoctoral Research Fellow
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to improve the early detection of pancreatic tumors and improve patient management. Different machine-learning approaches will be compared, and models validated on data prospectively collected. The position
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learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI
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, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning
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, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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protein foods including through high moisture extrusion. Key responsibilities will include: Explore innovative methods for food process optimization including the use of AI and machine-learning Develop and
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15 Apr 2026 Job Information Organisation/Company INESC ID Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions Bachelor Positions Application
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computational chemistry, reaction network analysis, and machine learning for organometallic catalytic reactions. 2. Design of membrane-permeable macrocyclic peptide drugs via machine learning structure