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of video and low-cost sensor technologies to capture subtle movement patterns, creating a rich dataset for AI-driven analysis. Machine learning, deep learning, computer vision and multimodal AI methods will
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-frequency eddy current sensor validated against real ROT measurements; (3) a real-time physics-constrained state estimator integrating JMAK transformation kinetics with the EM forward model; and (4) a model
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novel algorithms, aiming for theoretical guarantees when working with structured and unstructured data. Pursue and complete a PhD thesis within the appointed 4-year duration. Join the Vienna Doctoral
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satellites and space payloads. The laboratory features an advanced electronics development environment equipped for precision soldering, calibration, functional testing, and sensor characterization. A
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in eddy current sensor responses. This will involve both experimental work and analytical interpretation, linking EM signals directly to underlying physical mechanisms. Key research themes include
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-robot testing. Work could involve new algorithms, reasoning pipelines, foundation model evaluation, benchmarks, platform integration and practical deployment limits. It suits candidates interested in
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global data such as commodity prices and weather predictions. Technological progress has made it possible to automatically collect a variety of sensor data and self-reported practice. The challenge is to
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cognition simulation algorithms, benchmark evaluations against direct behaviour-to-label baselines, interpretable markers of cognitive-affective dysfunction, and prototypes for non-invasive mental health
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for next-generation intelligent audio IoT sensors. Your Role and Goals As a Doctoral Researcher, you will: Design energy-efficient digital in-memory computing circuits for on-chip AI inference, spanning both
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investigate the influence of trajectory accuracy on player perception and engagement within a dedicated laboratory environment. Developing distributed synchronization algorithms: we will design and implement a