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on the broader areas of cybersecurity, hardware security, privacy-preserving machine learning, and applied cryptography. Investigate methodologies, protocols and architectures for private and trustworthy
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to collaborate and learn from experts researching, developing, and testing emerging technologies in marine energy and/or blue economy. You will conduct research at both your academic institution and at an external
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skills. Candidates must possess excellent communication and interpersonal skills to work effectively in our team, as well as a willingness to learn new methods and techniques as the projects evolve
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 3 months ago
the structure from such data is challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine
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periods of time, repetitive motion related to computer work. Shift Monday-Friday 8:00am-5:00pm, weekends or evenings may be required by project work. This position is eligible for complete remote work. Job
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, biologics, and cannabis. Apply statistical and machine learning approaches (e.g., sequence analysis, latent class analysis, clustering) to examine medication use trajectories and patient subgroups
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considerations. Experience working with machine learning methods for control, perception, or decision-making in physical systems is an advantage. Knowledge of or a passion for sustainable computing
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Central China Normal University, Physics Position ID: CCNU-Physics-PDLQCD2025 [#29851] Position Title: Position Location: Wuhan, Hubei 430079, China [map ] Subject Areas: Machine Learning / Machine
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, fast scanning calorimetry (FSC), small- and wide-angle X-ray scattering (SAXS/WAXS), and synchrotron X-ray techniques is highly desirable. Knowledge of programming and machine learning is also strongly
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position) Designing a machine-learning-based bias correction method using retrospective forecasts and reanalysis data for comparative calibration. Topic 3: Development of seasonal prediction models (one–two