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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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applying machine learning and computational methods for protein design, in close integration with experimental enzymology and biocatalysis. The tasks include: Development and application of AI and machine
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Fundación para la Investigación Biomédica del Hospital Gregorio Marañón (FIBHGM) | Spain | 34 minutes ago
signal and image processing, machine learning, deep learning, robotics, and 3D design, with proficiency in programming languages such as JavaScript, R, Python, Matlab, or SQL. • Proven research experience
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., PyTorch, TensorFlow, HuggingFace). Model Development and Delivery Support Perform data cleaning, exploratory data analysis (EDA), and feature engineering. Train, evaluate, and compare machine learning
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neuroscience, and brain-computer interfaces, machine learning and deep learning, statistical modelling, regression methods, and uncertainty quantification, calibration, interlaboratory comparisons, and
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. Candidates with experience in dimension reduction, deep learning, machine learning, modeling neuroimaging data are especially encouraged to apply. Excellent written and communication skills are required
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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
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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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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