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machine learning methods in the context of biological systems Experience with programming (e.g., Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will be hosted
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biological signals. The project will focus mainly on developing innovative models for biomedical signals with irregular cyclicity and exploring potential machine learning applications. Position Objective
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Leonardo. The successful candidate will play a crucial role in developing and optimizing machine learning workflows for large-scale environmental data analysis, contributing to the creation of robust and
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. The findings will also support improved treatment design and layout, real-time decision-making during wildfire incidents, and the adaptive management and monitoring of fuel treatment investments
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machine learning applications. Position Objective : The primary focus of this position is to develop concentration inequalities in the nonstationary setting, specifically for periodic Markov chains and
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for construction operations. The successful candidate will contribute to cutting-edge research in mixed reality (MR)-based simulation platforms, machine learning-based process optimization, and human-machine
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city models focused on health and environmental infrastructures. Advanced Data Analysis: Advanced skills in machine learning, deep learning, and advanced statistics for processing complex data. Urban
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: Develop and implement machine learning algorithms for SOC and SOH estimation. Analyze large datasets from battery systems to improve model accuracy and performance. Conduct research on predictive analytics
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challenge: enhancing the resilience of avalanche hazard forecasting and monitoring in areas overlooking mountain roads, in a rapidly changing climate. We aim to pool procedures, snow and weather measurement
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analyzing urban data (traffic, energy consumption, environment). Strong skills in integrating IoT devices into complex digital systems. Advanced expertise in machine learning and artificial intelligence