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) parameters and state variables. Inferring these parameters and/or states from large amounts of possibly high-resolution data leads to computationally intensive inverse problems. The team aims at developing
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Tangorra (Diego Portales University, Chile). For more information on the project, see the link above. Applicants are invited to propose a project that falls within these parameters. The selected candidate is
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relationships between various parameters and to identify the mechanisms by which they operate. This abstraction must be based on empirical validation, but the mining of data alone is neither sufficient nor
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infrastructure. Multi-layer farming not only enhances crop yield per unit area but also enables farmers to more precisely control the growth process by adjusting key climate parameters. To investigate
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systems, with a focus on 3GPP compliant 5G/6G NR NTN OFDM waveforms Develop and analyse signal processing and/or machine learning algorithms for joint channel, delay, Doppler and carrier phase estimation
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aerodynamic models that remain computationally efficient enough for integration into real-time estimation and control loops. This postdoctoral position is part of the ReTwist research initiative, jointly
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demanufacturing and advanced AI. Building on our infrastructure for product identification, computer vision, and lifecycle assessment, we will launch the REINFUSE project in 2026, a four-year Flanders Make SBO
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‑based localisation or DOA estimation, design of ML models for time‑series or signal data Excellent programming skills in Python and/or MATLAB for signal processing and ML. Familiarity with libraries
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concrete (corresponding to 40% of cement use) and CEM I cement, leading to inaccuracies in estimating CO₂ capture, especially for other cement-based materials and modern blended cements. This project will
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in international research visits if needed. We are looking for a highly motivated researcher with: A PhD in machine learning, computer vision, remote sensing, glaciology, climate science, or a related