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- Delft University of Technology (TU Delft); Published 11 Nov ’25
- Delft University of Technology (TU Delft); yesterday published
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with signal processing and control algorithms. Excellent communication skills in English (written and spoken); ability to work in interdisciplinary teams and to publish scientific results. A strong
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opportunity to choose between two missions: • Mission 1: Improve new automated algorithmic schemes to quickly, efficiently and robustly detect and extract recorded geophysical signals related to earthquakes
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members Participate in traffic scenario generation project and pedestrian modeling project. Develop sophisticated AI-driven algorithms that create realistic, safety-critical test scenarios for autonomous
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outcomes. The individual will be expected to develop stimulation strategies and testing algorithms, write code, and develop software. They will do extensive validation and testing, under the supervision
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SD-26065 -POST-DOC IN METHOD DEVELOPMENT FOR HIGH RESOLUTION CHARACTERIZATION OF NOVEL SAFE AND S...
methodologies and algorithms for image fusion and co-registration Experience in writing, first authoring of publications and reports Interest in setting-up new academic and industrial collaborations Interest
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, and improve prediction accuracy - develop Machine Learning and AI algorithms for crop management, yield predictions and decision support systems - prepare manuscripts for peer-reviewed publications
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the JAX Python library, including efficient implementations of classical numerical algorithms. 2. Extend the hybrid FEA-ML framework to include nonlinear cohesive zone models with simple traction separation
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, chemical fixation and resin embedding, FIB milling, cryo preparation) Experience in developing and using correlative workflows, data processing and data visualisation, including methodologies and algorithms
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for the design and the benchmarking of algorithms tailored for photonic quantum computation. The outcome of the project will be the assembling of three prototypes: ● QSPACE, machine encoded in spatial modes and
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various processes in modern machine learning, including learning, inference, and generation. In particular, we are working to establish novel theories and algorithms that enhance the efficiency