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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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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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framework for understanding emergent deception in human-AI interaction by uniting behavioural-psychological, economic-strategic, and machine learning perspectives. The postdoc will be jointly supervised by
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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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of bioacoustic signals. Extensive experience with programming (Matlab, R, Python) including GPU programming is required, and familiarity with edge-based machine learning (particularly sound event detection), open
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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
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architectural firm. The candidate is expected to publish in leading Human-Computer Interaction venues. Your competencies You hold a master’s degree in human-computer interaction, computer science, interaction
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data integration and analysis Integrate phylogenomic and functional data using machine-learning approaches for candidate gene prioritisation Contribute to software and web-tool development Present