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machine learning methods for detecting, classifying, and identifying wireless anomalies in real-world radio environments. You will design and experiment with AI-driven approaches for spectrum analysis, work
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 8 days ago
science, and energy systems, with applications in smart mobility, electric vehicle integration, and power grid management. It contributes to the design of decision-support tools for infrastructure planning
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modelling methods to design resistance-proof antibiotics. You will join an interdisciplinary team, integrating machine learning, medicinal chemistry and microbiology. You will work with Asst. Prof. Eli N
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-driven applications. Demonstrated expertise in designing, implementing, and evaluating complex software systems. Strong programming skills in relevant languages (e.g., Python, C++, Java) and experience
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design and execute research studies, analyze complex datasets, and contribute to the development of new methodologies and therapeutic strategies. The position offers opportunities for interdisciplinary
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and technology comprehension tools for children investigating children’s understandings and conceptualisations of computational and AI-based systems designing and empirically studying learning
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, fostering innovation and real-world impact in keeping with Ohio States land-grant mission. This position provides opportunities to: Contribute to the development of CETEs emerging research and development
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, innovation and entrepreneurship in all areas Personalized learning program to foster our staff’s soft and technical skills Multicultural and international work environment with more than 50 nationalities
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at unprecedented resolution. The core innovation of your work will be integrating this data to train deep learning models that predict chromatin accessibility and gene expression patterns. These models will
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responses and microbial persistence. Learn to design, train, and iteratively refine machine learning models to learn distinguishing features of true microbial contaminants versus host-derived and process