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(or equivalent) in data science, applied mathematics, electrical engineering, or a related field. • Strong background in machine learning and/or statistical modeling. • Good written and oral communication skills
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, and system integration Providing general support for the lab Job Requirements: Master’s degree in Electrical Engineering, Computer Science, or related field Knowledge and experience in machine learning
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application of advanced machine learning techniques, including deep learning approaches, aimed at identifying complex patterns of host immune responses to vaccination. The research activities will also include
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of innovative multicriteria models for the management, integration, and enhancement of geological and geophysical data, using geostatistical, geomatic, and, where appropriate, machine learning techniques
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researchers to translate models into usable tools and optimize simulation inputs. User Workflows & Interfaces: Develop intuitive interfaces (CLI/APIs) and streamlined workflows for simulations. Machine Learning
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Fundación para la Investigación Biomédica del Hospital Gregorio Marañón (FIBHGM) | Spain | about 8 hours 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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(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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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