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therapeutic discovery and providing commercial growers sustainable methods to meet increasing global food demand. Responsibilities Apply machine learning techniques, statistical modelling, and chemometric
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of partial differential equations (PDE). Examples of models in the scope of the project include particle models, stochastic PDE and models from fluid dynamics and machine learning. What skills are important in
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Engineering, Biomedical Engineering (Medical Informatics), or related areas. Recipient category: Masters, enrolled in the course: Degree courses: enrolled in doctorate. Non-conferring degrees courses: enrolled
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optimization. Experience with quality-diversity methods is a plus. • Experience with machine learning and artificial intelligence. • Strong programming skills (e.g., Python, C++), and familiarity with ROS
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: • Graph-based learning and community detection: Identify cohesive and antagonistic groups within signed networks. • Machine learning and network embeddings: Measure consensus, polarity, and opinion shifts
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
be predicted using machine learning based on drug-specific information, patient demographics, and clinical trial data. 2. Modeling for Regulatory Science – Leveraging drug development and regulatory
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RE-C05-i08 do Programa de Recuperação e Resiliência, através da Fundação para a Ciência e a Tecnologia - FCT, nas seguintes condições: Scientific Area: Computer Engineering, Biomedical Engineering
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proficiency in Python (e.g., NumPy, Pandas, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with supervised
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of integrating advanced optical technologies with machine learning techniques to develop novel, high-performance fibre-optic sensing applications. You will be responsible for the application and validation
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to facilitate the integration of the framework with external systems and educational platforms; Establish a Machine Learning Operations (MLOps) pipeline to automate the lifecycle of models, including training