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adaptation; reinforcement learning and inverse reinforcement learning. o Machine Learning & Intelligence, including machine learning and adaptation; deep learning; computer vision; machine intelligence
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measurements from real projects, statistically analyse them, and conduct experiments with modern machine learning techniques and generative AI. A strong background in software engineering as well as some
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MICIU/AEI/10.13039/501100011033/. Website for additional job details https://www.cvc.uab.es/blog/2025/11/21/postdoc-position-on-continual-learning-o… Work Location(s) Number of offers available1Company
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for all UNLV postdocs; and provide professional development programs and networking events for postdocs. UNLV currently employs postdoctoral scholars across a wide range of disciplines. Learn more about
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Machine/Deep learning and classification Knowledge of the Linux operating system for using a computing cluster Interest in transdisciplinarity and teamwork Autonomy and scientific rigor Website
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software related to the medical field Experience of specific software and programming languages, specifically ones suitable for machine learning, e.g. PyTorch or TensorFlow. Strong ability in spoken and
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by ARPES, pursue scalable wafer-scale moiré epitaxy, develop epitaxial superconductors for quantum computing and integrate machine learning for automated high-throughput MBE. We are particularly
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predictive modelling; Bioinformatics and Knowledge Graphs (visualization and reporting); AI-based data integration across cohorts (with federated machine learning); Contribute to ongoing projects, such as: o
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enhance machine learning performance; novel chip design strategies prioritizing efficiency and cost; verification of digital designs; advancements in electronic design automation (EDA), especially for AI
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | 15 days ago
hyperscanning neuroimaging data, using advanced statistics and machine learning methodologies for temporally-sensitive data, such as GLMM, Random Forests, LSTM, etc.. Use of MatLab for pre-processing, and