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strong publication record (first-author papers in high-impact journals preferred). Demonstrated expertise in at least two of the following areas: AI/machine learning for biological modeling (e.g., virtual
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uploaded using the dedicated electronic form. helpdesk: petra.koudelova@fsv.cvut.cz Physics-guided learning for machine control Description: Robust machine control assumes modeling of robot-environment
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optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos. Become a part of our team and join us on our journey of research and innovation! What
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Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling, machine-learning model development, structural sensing and health monitoring, conducting physical
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models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and algorithms Python and relevant libraries (e.g., PyQt, OpenCV, NumPy, scikit-learn), particularly
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and image analysis within the project, responsible for designing and iterating on machine learning architectures, managing training pipelines and datasets, and optimizing models for deployment across
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). Proficiency and independence in developing Python‑based machine learning / artificial intelligence models (e.g., LSTM or time-series models) to predict the long-term properties of bioactive materials (mandatory
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candidates who can evidence their experience with as many of the following areas as possible, preferably at least two: Interdisciplinary mathematical modelling Machine learning and/or other quantitative
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surveillance and preparedness planning using multiple modeling approaches. The successful candidate will develop and implement statistical and machine-learning models, integrate multi-source ecological datasets
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(EMG), to capture detailed motion, interaction forces, and muscle activity. Predictive Physiological Modeling: Development of machine learning models capable of anticipating motion intent while