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Field
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of radiative transfer models to study various essential climate variables (clouds, aerosols, surface properties, greenhouse gases); (iii) validation activities. The postdoctoral researcher will work under the
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experience in computational biology or cancer genomics Experience with high-performance or cloud computing (e.g., HPC, AWS, GCP) At least one first-author peer-reviewed publication Strong communication and
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security mechanisms connected to the cloud in residential environments and intrusion detection methods in the context of embedded systems and IoT devices. The work will explore machine learning techniques
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for improved interpretability and generalization. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability to work collaboratively in interdisciplinary and cross
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and Machine Learning tools and algorithms to solve hydrology and water resources problems. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability
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at a later stage however, currently available and relevant examples include CIC-IDS2017(4), CSIC 2010 HTTP(5), and BCCC-cPacket-Cloud-DDoS-2024(6). • Conduct initial analysis of candidate features
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algorithms to solve hydrology and water resources problems. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability to work collaboratively with
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libraries like PyTorch or ONNX. Demonstrated experience working with large-scale data processing and deployment (local and cloud) including SQL database technologies. Demonstrated ability to represent complex
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learning and cloud-based architecture skills to lead the fine-tuning/training, design, development, and integration of the AI components of the AiCT-Med platform. Candidates from diverse backgrounds in AI
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regular meetings with the research group. Ph.D. in Electrical Engineering, Computer Engineering, Computer Science, or a related field. Proven experience working with LiDAR sensors, point cloud data