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Field
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project involves interdisciplinary research at the interface of computer science and mathematics, with a focus on bivariate molecular machine learning for modeling molecular interactions and properties
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) approaches. Design predictive maintenance algorithms using machine learning, statistical learning, and digital twin-based models to anticipate failures and optimise maintenance interventions. Integrate AI
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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 19 days ago
systems Computer science » Informatics Information science Information science » Information management Researcher Profile Recognised Researcher (R2) Application Deadline 31 Mar 2026 - 12:30 (UTC) Country
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: Investigate and design optimal computing and communication architectures for hardware acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical
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, materials science, and physics. Supported by 19 countries, the ESRF is an equal opportunity employer and encourages diversity. Context & Job description Thesis subject: Machine Learning for Neutron
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agentic AI (e.g., large language models, algorithmic systems) shape users’ psychological and behavioral well-being, particularly among vulnerable populations. Applicants are encouraged to approach
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General Summary of the Position Postdoctoral positions in Deep-Learning Omics are available in the Zhou Lab (https://profiles.umassmed.edu/display/20062865 ). The Zhou Lab at UMass Chan Medical
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biofoundries, fostering sustainable industrial biotechnology across the bioeconomy sectors. The lab combines synthetic biology, machine learning, and biosensor and pathway dynamic regulation design to produce
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
. Picchini. Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings. Transactions on Machine Learning Research, 2024 Kugler, F. Forbes, and S. Douté. Fast
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Intelligence/Machine Learning (AI/ML) methods in agriculture (Agro-AI/ML); and Experience in programming with multiple languages (e.g., Java, C/C++, Python) for geospatial information systems, agro-informatic