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locomotion. Apply machine learning and machine vision algorithms to track body and limb movements. Use biomechanical modeling to analyze walking data and fit locomotion models. Operate a force sensor to
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electric machines•Nanomaterials for next-generation electronic and photonic nanodevices•Oxide materials for MEMS piezoelectric and multiferroic sensors/actuators•Solid-state devices such as solar cells
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the position description. Preferences The ideal candidate will have experience with wearable sensors and/or remote monitoring approaches and strong programming (e.g., R, Python, or similar) and writing skills
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anticipated include imaging phantom-based evaluation of quantitative SPECT/CT; reconstruction algorithm development; establishing a secondary standards laboratory for unsealed source metrology through gamma
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and implementing innovative solutions at the intersection of sensor data collection, machine learning, and real-time decision-making. Specifically, the candidate will contribute to projects focused
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the Institute of Sensor and Actuator Systems, in the Research Unit of Microsystems Technology, TU Wien is offering a position as project assistant (PhD position). The PhD position is planned for a total duration
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and training. -Support processing and integration of datasets, including UAV imagery, satellite data, IoT sensor data, and field observations. -Contribute to workflows for AI/ML applications, crop
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generative modeling and representation learning, domain adaptation, ML-assisted lattice field theory, and quantum algorithms and Hamiltonian simulation for high-energy physics, etc. The CTPU-PTC group, whose
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candidate will play a key role in designing and implementing innovative solutions at the intersection of sensor data collection, machine learning, and real-time decision-making. Specifically, the candidate
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preprocessing IoMT network traffic datasets. Implement and evaluate machine learning algorithms (e.g., logistic regression, SVM, random forest) for intrusion detection. Develop prototype software tools (e.g