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new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health records
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). Familiarity with machine learning techniques, particularly LSTMs or other deep learning architectures. Experience with large datasets, geospatial analysis, or database development. Knowledge of ecological flows
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for use by the research and lay communities worldwide with access through computer query protocols and user-friendly interfaces. NED provides data for more than 1.1 billion objects and is growing rapidly
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statistics, and statistical programming packages. May utilize artificial intelligence and/or machine learning techniques/methods. Data Management Focuses on ensuring the efficient and secure handling of data
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have an interest in quality news and AI-based chatbots. We expect: A Master’s degree in either computer engineering or computer science/data science. Experience with AI, Large Language Models, RAG, data
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CeMM - Research Center for Molecular Medicine of the Austrian Academy of Sciences | Austria | 24 days ago
the Medical University of Vienna, the Technical University of Vienna and University of Vienna, the AITHYRA and CeMM PhD programs for both life scientists and computational scientists/machine learning experts
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research profile, and an international network around big data in marine sciences. The candidate will have access to NIOZ’s high-performance computing cluster, GPU nodes for deep learning, dedicated data
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2,900 work in administration and organisation. We are looking for a/an University assistant predoctoral/PhD Candidate Optical Quantum Computing and Machine Learning 51 Faculty of Physics Startdate
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necessary. Strong computer proficiency with Windows, MS Word, Excel, and PowerPoint is required. Ability to use scanning software and knowledge of the Internet necessary. Strong typing skills required
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and Data-Driven Discovery, which involves creating a large, unique dataset linking composition to phase stability and fundamental mechanical properties for data-driven down-selection. The second pillar