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Antimicrobial resistance (AMR) is a major threat to global public health, causing millions of deaths each year. We are seeking a postdoctoral researcher to develop machine learning and generative
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involve developing an approach that uses Knowledge Organization (KO) metadata and ontologies to optimize parallel processing and scheduling policies (via Kubernetes) for Machine Learning tasks. The fellow
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commensurate with abilities and experience Type of Position Other Position Time Status Full-Time Required Education PhD in Engineering Required Related Experience see Skills / Knowledge / Abilities Required
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-driven machine learning approaches, they will improve our understanding of nutrient flows in agricultural landscapes. The postdoc will contribute to the development of databases representing the current
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- 4 Additional Information Eligibility criteria • Experience in computer modeling and programming • Knowledge of associative learning at both the neurobiological and psychological levels • Experience
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candidate who is passionate about advancing scientific knowledge at the intersection of machine learning and genomic perturbation modelling. The ideal applicant brings not only strong technical skills, but
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commensurate with abilities and experience Type of Position Other Position Time Status Full-Time Required Education PhD in Engineering Required Related Experience see Skills / Knowledge / Abilities Required
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within the Great Basin as well as enhancing the knowledge of the genetic links between intrusive activity and critical mineral metallogeny within Nevada, delineating new target areas for mineral
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machine learning models directly on these edge devices for real-time anomaly detection and identification. You will develop robust signal acquisition and processing pipelines, translate research-grade
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 13 days ago
) Optimization and parameter identification methods Data-driven modeling and machine learning Physics-Informed Learning (or hybrid modeling approaches) Handling and analysis of large-scale datasets (e.g., mobility