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methodological development and application of bioinformatics, biostatistics, machine learning, and data management within clinical research. CLINDA is interdisciplinary and employs biostatisticians
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intelligent control and aerial robotics for navigation in uncertain environment. You will be mainly responsible: for implementation of machine-learning algorithms for unmanned aerial vehicles; validation
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(satellites, drones, etc.) through AI and machine learning; 3) validation and feasibility of the introduced technologies through full-scale pilot scale, demonstration, documentation, and simulations
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) the application of remote sensing (satellites, drones, etc.) through AI and machine learning; 3) validation and feasibility of the introduced technologies through full-scale pilot scale, demonstration
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degree in computer science, mathematics, statistics, physics or relevant fields. Strong background in machine learning, preferably experience in probabilistic modeling, Bayesian machine learning, or graph
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logical way and parametric simulation engines. Familiarity of machine learning / AI techniques and custom LLMs generation is beneficial. Knowledge of life cycle assessment (LCA) and LCA methods, carbon
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the Machine Learning, Artificial Intelligence, or Robotics, reflected through contributions to major conferences (ICLR, IEEE ICRA, NeurIPS, ICML, CVPR, ECCV, SIGGRAPH, ICCV, etc.) Solid mathematical and
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Machine and Deep Learning – Applications and Case Studies in Business AI Agents and Agentic Workflows in Business Explainable AI (XAI) E-commerce, Web and Social Media Analytics Big data analytics Natural
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industrial energy systems that combine physics and data to become adaptive, autonomous and trustworthy? To get there, you will work at the intersection of thermal energy systems, machine learning and
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data sources (e.g., registry data, surveys, and organisations). Your competencies Digital methods such as machine learning based classification, computational text analysis, network analysis, web