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learning as well as a strong background in scientific programming (in languages like Julia, Python, Fortran or C/C++). The applicant must hold a PhD in physical oceanography, atmospheric sciences, computer
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strategies within it. You will combine archaeological and landscape data (maps, satellite imagery, archaeological datasets) with machine learning approaches to build a system that highlights promising
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machine learning approaches to build a system that highlights promising locations for archaeological research. You are also expected to play an active role in project coordination and in writing follow-up
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an advantage: plasma surface functionalization; electrode/electrolyte interfaces; battery degradation modelling; microstructure-resolved modelling; tomography or image-based electrode modelling; machine learning
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-level modelling of environmental exposures and health risks; Interpretable and uncertainty-aware machine learning for heterogeneous health data. This position offers the opportunity to work in a
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hold a PhD in machine learning, computer science, bioinformatics or equivalent. You combine strong analytical skills with the ability to work independently and lead collaborative efforts. You are a team
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, such as technical testing platforms for AI. Your profile PhD in Computer Science, with a thesis related to machine learning, software engineering, software security or a related area Strong programming and
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-scale nature, complexity, and heterogeneity of 6G networks, we use tools such as artificial intelligence/machine learning, quantum computing, graph theory, graph-signal processing, and convex/non-convex
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Microeconometrics Strong interest in methodological issues in microeconometrics, machine learning, high-dimensional models Proven ability to autonomously conduct research at a post-doctoral level Proven ability
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knowledge of developing AI-based information systems, such as AI-enabled engagement e.g., agentic AI and AI-enabled insight generation e.g., machine learning analytics in both individual and organizational