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The ideal candidate applies machine learning and big data techniques to important questions in economics, combining advanced computational methods with sound economic theory to uncover insights
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DC-26094– POSTDOC/DATA SCIENTIST – AI-DRIVEN CLIMATE RISK MODELLING AND EARLY WARNING SYSTEMS FOR...
abiotic resources. We integrate remotely sensed information with in-situ data, process-based models, and leverage satellite communication, IoT and machine learning technologies in order to provide evidence
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bioinformatics for immunology research programs. You'll work at the cutting edge of AI-enhanced immunology, applying deep learning, foundation models, and advanced machine learning approaches to understand how
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Machine Learning, addressing challenges in counter drone swarm formation and defense Design, develop and conduct experiments of drone swarms using both simulation environments and real-world deployments
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/R, machine learning frameworks, and dashboarding tools (e.g., Streamlit, Superset, Grafana, PowerBI). Familiarity with various types of databases, including NoSQL (e.g. MongoDB), graph databases (e.g
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, Mobility or Climate, among others. Strong programming skills in Python/R, machine learning frameworks, and dashboarding tools (e.g., Streamlit, Superset, Grafana, PowerBI). Familiarity with various types
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approaches - such as machine learning, artificial intelligence, or other data-driven methodologies - will be an asset. This position is part of the University of Luxembourg's tenure-track scheme, which offers
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, machine learning, robotics, aerospace engineering, and/or image/signal processing Experience with European, national, and/or industrial projects A solid mathematical background Proficiency in Python, Matlab
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Machine Learning (ML) to detect anomalies (such as a new unknown possible entry point) and provide actionable recommendation according to the recovered attack surface. A tool like AMASS[1] from OWASP
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develop machine‑learning models that learn from and build upon these pNTA results. The successful candidate will be supervised by Prof. Dr. Emma Schymanski and Dr. Federica Piras. For further information