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). - Familiarity with machine learning principles and generative/classification models (PyTorch Lightning, torch, scikit-learn, etc.), as well as data/model analysis methods (PCA, t-SNE, etc.). - Proficiency in
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middleware (e.g., ROS, MoveIt) and hardware integration. Knowledge of machine learning, reinforcement learning, or vision-language models for robotics is a plus. Hands-on experience with robotic arms (e.g
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, bioinformatics, biomedical data modeling and ontologies, biomedical natural language processing and information retrieval, health artificial intelligence and machine learning, privacy technology, global health
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for enhanced durability, performing microstructural analysis and mechanical testing. Topology Optimization & AI Integration: Use AI and machine learning to guide structural and topology optimization, creating
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position in biomedical informatics is available at Harvard Medical School to work at the intersection of advanced machine learning and large-scale biomedical data. The selected fellow will join a dynamic
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Summary Learning Commons Tutors support the academic success of all BGSU students by providing assistance in a variety of subject areas, including math, statistics, writing, and other course content
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, Massachusetts 02215, United States of America [map ] Subject Area: Computer Engineering / machine learning and other related areas Appl Deadline: none (posted 2026/01/30 05:00 AM UnitedKingdomTime) Position
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published literature to generate quantitative analysis, Some Python or Matlab programming may be required to employ in the development of machine learning algorithms. In these capacities, the Research
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 25 days ago
machine learning applied to natural language processing, computer vision, or a related area. Strong publication record in NLP, ML, or related areas -Strong programming skills, including TensorFlow and/or
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explicit model of the biophysical effect of land use change, a machine learning emulation of dynamic global vegetation models. Both activities aim to improve understanding and quantification of the effects