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algorithms to shape the liveable cities of tomorrow? Job description Human-centred AI techniques, such as Reinforcement Learning from Human Feedback (RLHF), hold great potential for supporting design
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skills and fosters employment opportunities in the field of cultural and environmental heritage at both national and international levels. Main supervisor(s): Dr Clotilde Boust (C2RMF & CNRS-SATIE) Prof
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vehicle (AV), allowing for automated detection, prediction, mapping, and planning. During the vehicle’s operation, data is obtained through a myriad of sensors in an AV—including RADAR, LIDAR, cameras, and
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(GPR), complemented by soil sensors and borehole data. A particular emphasis will be placed on the combined use of borehole and surface GPR, as well as small-scale EMI measurements in controlled
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subsurface physical measurements, including Electrical Resistivity Tomography (ERT), moisture and permeability sensing, ultrasonic testing and potential mapping. By means of innovative algorithms you will
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, architecture, and development of prototype and product versions of our semiconductor test tools. Translate research algorithms into production-grade, maintainable software. Build and manage a small high
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Autonomous Scalable Knowledge Extraction and Decision Making for Complex Systems and Dynamic Environments School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof
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Max Planck Institute for Intelligent Systems, Tübingen, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | about 1 month ago
: Large-Space Gaze Analysis via Multi-camera System for Social Interactions” to develop datasets and algorithms to capture and analyze eye gaze. About the project We are supporting the development of highly
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by combining psychological profiling, biological lab data, physiological time series, and sensor data. The postdoc will play a leading role in developing and implementing predictive algorithms designed
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Prof Lyudmila Mihaylova Application Deadline: Applications accepted all year round Details This research project focuses on the development of methods for intelligent wildfire detection and localisation