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neurodegenerative disease, computational. Knowledge, Skills and Abilities: Strong theoretical and applied knowledge of machine learning, deep learning, and statistical modeling. Strong data wrangling and
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National Research Council - Research Institute for Geo-Hydrological Protection | Italy | about 7 hours ago
of human presence and urban indicators. The required analysis are implemented within the GRASS GIS environment using geomorphometric and machine learning methods Where to apply Website https
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conversational AI and multimodal interfaces, Bias detection and fairness-aware tools for ethical AI. Integrate machine learning models, visualization dashboards, and backend services. Contribute to data collection
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, microbes will be identified that affect fermentation outcomes. Building on previous work from the host group (doi:10.1016/j.chom.2023.05.024; doi:10.1093/nar/gkaf1388), we will develop machine learning tools
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learning or multi-agent systems. Experience with cloud-native technologies (Docker, Kubernetes) or distributed computing. Experience with efficient neural architectures, scalable model design, or resource
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infrastructure (e.g., turbine components). Research on advanced deep learning techniques, including architectures based on GRU, LSTM, attention mechanisms, and hybrid models. Implementation of real-time predictive
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responsibilities? No Preferred Education/Experience Bachelor’s degree in Computer Science, Machine Learning, AI, Data Science, Engineering, Mathematics, or related field (Master’s or PhD preferred). 7+ years
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activities. Where to apply Website https://jobs.fbk.eu/Annunci/Offerte_di_lavoro_Tenure_track_position_for_a_Resea… Requirements Research FieldEngineering » Computer engineeringEducation LevelPhD or equivalent
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to reduce the cost of clean hydrogen to $1/kg by 2031. The project proposes to address key scientific challenges by using molecular simulations (reactive force fields like ReaxFF and machine learning
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SUBATECH. The proposed thesis is part of the research activities of the team RAMBO of the RAMBO team (Robot interaction, Ambient systems, Machine learning, Behaviour, Optimization) of the Lab-STICC