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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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Apply advanced data mining, statistical modeling, graph algorithms, and machine learning to extract insights from large structured and unstructured datasets Mentor junior data scientists and analysts
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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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Packages/Procedures: Update, review, manage, and run daily, monthly, and quarterly reporting packages, with a keen eye on data quality and robustness, and distribute them to relevant parties within
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experience in radar research, developing signal processing algorithms for long-range ultra-broadband Synthetic Aperture Radar systems and short-range FMCW systems. In recent years, breakthroughs in
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methods (logic, theorem provers, type systems, categories, etc.), concurrent programming languages (choreographic programming, session types, etc.), distributed computing (cloud computing, microservices
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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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which of the following general areas best describe your research interests (you may select up to three): Algorithms, Theory, and Foundations Artificial Intelligence and Machine Learning Computing
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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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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | about 2 months 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