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systems. Ths position requires a deep understanding of X-ray Absoprtion Spectroscopy and prior experience with methods of machine learning and artificial intelligence. A highly competitive candidate would
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. ●Deep knowledge of political science, public policy, and/or Arizona history. ●Knowledge of preservation and conservation standards for archival materials. ●Demonstrated effective interpersonal and
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‑on experience with common machine learning / deep learning frameworks (eg. PyTorch or JAX) applied to biological or structural data. Solid Python programming skills, with experience building maintainable and
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problems, statistical learning and machine learning (machine learning, deep learning) - Knowledge of associated software development tools and environments: Python, PyTorch, Scikit-learn, Jax, Julia
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | 2 months ago
deep learning (x/f/d/m) Background With the project Deepcloud, we will leverage the machine-learning revolution to understand clouds and their role in the climate system. We aim to train a deep learning
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consider whether your values align with our Guiding Values and Principles , Principles of Community , and Strategic Plan . At UC Berkeley, we believe that learning is a fundamental part of working, and
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, deep learning, statistical modeling, and AI system design applied to biological or agricultural systems. Experience architecting scalable AI pipelines, including model evaluation, deployment, and
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Depending on class schedules. Sensitive Position No Job Description General Description: NOVA Workforce is seeking English as a Second Language (ESL) instructors to teach in-person Intensive English and Part
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academic and professional qualifications suitable for appointment as a tenured or tenure-track associate professor or full professor in the HSMP department. We seek candidates with deep expertise in health
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YOUNG RESEARCHER IN THE FIELD OF EARLY DETECTION OF THE HEALTH STATUS OF PLANTS USING REMOTE SENSING
and features for the pre-symptomatic detection of changes in the physiological status of plants, developing, training, validating, and comparing predictive machine learning and deep learning models