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Field
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combines: Fluid dynamics and heat transfer (theory and experiments), Computational modeling, and Machine learning / computer vision for data analysis and pattern recognition. The goal is to improve
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enhance real-time decision-making in road traffic management. The project aims to bridge the gap between recent advances in AI and machine learning, in particular, multimodal and instruction-tuned
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on the electrical machine optimization to achieve high energy-efficient drive system. The practical part of the work will be testing the control model with a prototype machine in the research laboratory. Research
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built to identify and correct errors, apply bias adjustments, and assess data quality. State-of-the-art multisource blending methods will then be applied (e.g. kriging, probabilistic merging, machine
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of perceptual foundation models, using advances in deep machine learning and computer vision. The goal is to invent, develop and evaluate novel methods for pre-training and fine-tuning of perceptual foundation
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-tuning algorithms. What you will do You will carry out research and development in the areas of perceptual foundation models, using advances in deep machine learning and computer vision. The goal is to
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attachment. Main tasks Develop machine learning models to produce forest information at local and landscape scales Develop machine learning models for sustainable forest management Compare machine learning
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will contribute to projects focused on developing advanced machine learning models to quantify phenotypic traits of crops, including corn, soybean, and other selected species. These models will leverage
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computer vision models for forest-based 3D point cloud data. In recent years, large advances have been made for deep learning algorithms for high-resolution point clouds from small geographic areas. We seek
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-scale modelling, machine learning) High resolution analysis, monitoring of chemistry, structure and transformations at the atomic scale of buried interfaces and defects by correlated experimental