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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
, cloud computing, and AI. Minimum Education and Experience Requirements Ph.D. in Bioinformatics, Biomedical Informatics, Computer Science, or a related field. Required Qualifications, Competencies, and
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frameworks and libraries (e.g., TensorFlow, PyTorch). Ability to develop and implement AI models for business applications. Knowledge of cloud computing platforms (e.g., AWS, Azure) for deploying AI
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platform. The successful candidate will (1) integrate heterogeneous sensors and onboard computing hardware; (2) develop methods for LiDAR-based simultaneous localization and mapping (SLAM), autonomous
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-Performance Computing (HPC), scientific Artificial Intelligence (AI), and scientific edge computing. We are a leader in computational and computer science, with signature strengths in high-performance
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/agentic infrastructure for data and model discovery, model metadata extraction, and scholarly intelligence. The project will explore conceptual frameworks and system architecture to support data trust in
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natural language processing methods for analysis of unstructured clinical and biomedical data. 4. Experience with cloud-based and high-performance computing environments to support scalable data
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. You are encouraged to visit the ESA website: https://www.esa.int/ Field(s) of activity/research for the traineeship Main objectives of the Research Fellowship Cognitive cloud computing in space (3CS
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methods and anomaly detection techniques for complex systems Experience with sensor networks, IoT platforms, or real-time data acquisition and telemetry systems Familiarity with cloud computing
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geospatial analysis, environmental databases, and cloud or high-performance computing workflows Demonstrated record of peer-reviewed publications Experience collaborating with interdisciplinary teams
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sensing data, including data processing and analysis using tools such as GDAL, Google Earth Engine, or similar platforms, and working in cloud or high-performance computing (HPC) environments