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DC-26094– POSTDOC/DATA SCIENTIST – AI-DRIVEN CLIMATE RISK MODELLING AND EARLY WARNING SYSTEMS FOR...
abiotic resources. We integrate remotely sensed information with in-situ data, process-based models, and leverage satellite communication, IoT and machine learning technologies in order to provide evidence
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to conduct applied research (TRL>1) in the domain of quantum computing and/or machine learning; Possibility to file patent applications within the project; Funds to employ 3 other researchers: 1 postdoc and 2
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-France 75 005, France [map ] Subject Areas: Machine Learning Statistical Physics Appl Deadline: 2026/01/16 04:59 AM UnitedKingdomTime (posted 2025/11/04 05:00 AM UnitedKingdomTime, listed until 2026/05/05
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engineering Engineering » Communication engineering Researcher Profile Recognised Researcher (R2) Positions Postdoc Positions Application Deadline 20 Apr 2026 - 23:59 (Europe/Warsaw) Country Poland Type
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- Knowledge of mathematical probability and statistics, and optimization methods - Knowledge of machine learning, including supervised and unsupervised learning, deep learning, and model evaluation - Knowledge
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representativeness - Knowledge of software engineering for AI applications - Knowledge of mathematical probability and statistics and optimization methods - Knowledge of machine learning including
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described in our strategic vision, Pro Futuris, and academic strategic plan, Illuminate. Connections working at Baylor University More Jobs from This Employer https://main.hercjobs.org/jobs/22078479/postdoc
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the application of these methods to problems in the physics of oxides, semiconductors, metals and their surfaces. Machine learning methods are used to close the complexity gap. Currently, the group consists
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to study chromatin and gene regulation in mammalian cells and human disease systems. Current ongoing projects include: statistical modeling and advanced machine learning/AI method development for predicting
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scientific database software - Demonstrated expertise in chemical structure analysis by computer - Demonstrated facility in applying mass spectral fragmentation rules for electron ionization