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Bayesian computational methods for such (ill-posed) inverse problems and aims both at increasing their validity and at reducing their computational cost. In this project, we will focus on increasing validity
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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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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
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/ Deep Learning (particularly Computer Vision or 3D perception) Verification & Validation (V&V) of advanced algorithms, software or systems Formal Methods Safety Engineering / Safety-Critical Systems
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of medieval Latin. Excellent scientific writing skills, with a high production rate. Minimal comfort with computer tools and quantitative methods. No technical expertise is required, but a minimum level of
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4 Oct 2025 Job Information Organisation/Company Ghent University Research Field Computer science » Database management Computer science » Other Engineering » Computer engineering Engineering » Other
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, and brain computer interface tasks. This project will support our goal of testing the Controllosphere Theory of cognitive effort (see doi 10.1037/rev0000467). A Ph.D. and background in computational