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Field
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computing to develop a continuous and local alternative to existing gradient-based learning rules, bridging theories of predictive coding with event-based control/ Simulate models of the learning algorithm
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Environment (VTE) for disaster response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and resilience-informed design
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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1
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experience in developing computational models and implementing models for computer simulations. Software development in C++ and/or Python is expected, and experience in model analysis and parameter
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Perform molecular cloning, protein expression, and biochemical assays Conduct growth chamber experiments with model plant species Analyze genomic and transcriptomic data using computational tools Present
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used to measure motion and deformation. It provides comprehensive full-field deformation data, essential for analysing complex materials, structures and model validation. The DIC community has developed
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vivo evaluations using soft-tissue models to simulate gastrointestinal navigation and refine the system’s interface and functionality based on clinician feedback. Contribute to preclinical deployment
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Virtual Training Environment (VTE) for disaster response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and resilience
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complemented with computational simulations of mechanobiology-mediated angiogenesis, to further dissect the contributions of cell signaling and environmental mechanical properties. The research will be conducted
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on well-established model organisms, in our lab we investigate the mechanisms of adaptation in understudied bacterial species. The candidate will use experimental evolution to investigate how bacteria with