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Field
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Assistant Professor (tenure-track) and Associate Professor (tenured) Positions in Computer Scienc...
systems, programming of critical software and infrastructure, high-performance computing, and distributed systems. Model-driven Software Engineering Cybersecurity, including security by design, blockchain
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algorithms, capable of distributed learning on high performance and edge computing; The design of architectures/models which accurately capture the complexities of the data, with robust estimates of confidence
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topics as well as related topics (e.g., privacy, trustworthiness, mobile/wireless security, distributed learning and cloud security, software and system security). Areas of theoretical computer science
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challenge. The ESA-funded LISA will rely on an extensive ground segment for data processing and analysis. The LISA ground segment is a distributed facility (the LISA Distributed Data Processing Center
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candidate should be able to conduct research work independently To learn more about the work this group does, check out the following link: https://www.nrel.gov/grid/distributed-energy-resource-management
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: Neural networks and machine learning. Algorithm. Professional Experience: In the use of Python (PyTorch, TensorFlow) and C for the development and optimization of deep learning algorithms. Experience in
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architectures, frameworks, and prototypes for high-fidelity visual simulations and AI-enabled environments and applications. Apply computational models, data-driven algorithms, and physics-based simulation
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cell-tracking algorithms, we can follow thousands of individual cells in real time as they respond to carefully designed chemical and mechanical cues. These approaches generate uniquely rich datasets
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and/or statistical algorithms to classify building and land-use types relevant to electrical consumption Label and prepare training data for AI models; develop automated pipelines for classification
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | about 1 month ago
follows a phased algorithm: 1) generate an initial training set by uniformly sampling input points 2) (re)train the model on the trainng set 3) use feedback from the model’s performance to generate/augment