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Field
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wearable-sensor (IMU) data and human motion analysis. Solid programming skills (ideally, in Python) for algorithm development, control design, and data analysis. Confidence and interest in testing and
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Engineering, Biomedical Engineering, Computer Vision, Robotics, or a related discipline. A strong curiosity for human movement analysis and markerless motion capture. Experience with pose estimation, computer
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mechanical modelling, such as finite element analysis (FEA). experience of working with healthcare professionals and patients. strong knowledge of statistical methods, including non-parametric methods
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PhD in Physics, Applied Science, or a related discipline Experience in statistical physics, stochastic processes and/or data analysis methods Strong interest in engaging and collaborating with
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, Mathematics, Bioengineering or a related discipline PhD in Physics, Applied Science, or a related discipline Experience in statistical physics, stochastic processes and/or data analysis methods Strong interest
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expected to actively contribute to the project, work collaboratively as part of the research team, and perform the following major tasks: Conduct theoretical analysis, algorithm development, and simulation
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working with remote sensing and AI-based data analysis; Scientific publication record; Willingness to work in a collaborative environment. Application Materials Application Process: Apply online at https
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feedback. Classroom implementation and evaluation. Support and co-lead pilot studies in real CBL classrooms; contribute to data collection, analysis, and interpretation of the agent's performance, attending
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of the flow systems Data analysis Assisting the PI in publications writing and presentations Participate in weekly meetings and help to prepare reports and presentations for project sponsor Minimum
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the Saharan Desert. Nice to have: Experience with Landscape Evolution Modelling. Experience with Low-Temperature Thermochronology and/or geochronology. Experience with Source-to-Sink analysis. TU Delft (Delft