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- Delft University of Technology (TU Delft); Published 11 Nov ’25
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applications for a Postdoctoral Researcher position. The selected candidate will undertake both collaborative and self-directed research on mathematical modeling, optimization and simulation models/algorithms
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findings regularly. Prototype algorithms, validate outputs, and document methods clearly Collaborate asynchronously with an international team, present findings regularly Experience with remote sensing
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working under the supervision of Prof. Jaideep Vaidya (the PI and Director, I-DSLA) to develop and analyze privacy-preserving solutions for biomedical data research, implementing the developed algorithms
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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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lab/field validation. - Strong DSP/algorithms and real-time control; proficiency in Python/Matlab. - Experience integrating optics with electronics and firmware. - Track record of publications in fiber
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) for the high-luminosity phase of the LHC, in particular on its mechanical design, on the generation of the L1 trigger primitives, and on the development of offline reconstruction algorithms. In addition, it is
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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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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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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