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and physical systems, including topics such as differential geometry, invariant theory, algebraic and metric geometry, symmetry, geometric data analysis, and infinite-dimensional geometric structures
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: refining the project’s research questions, interview protocols, coding frameworks, and analytic approaches; leading one or more strands of data collection and analysis, including semi-structured interviews
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and explanation within dynamic contexts. Possible research directions include mechanistic interpretability of multimodal models, concept-based explanations grounded in affect and behaviour theory, and
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mechanics and propulsion, for which the AEROSQIN project constitutes a perfect framework. The candidate will advance techniques for large-scale 3D particle tracking by merging measurement theory and practice
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of the project and help shape its further development through activities such as: refining the project’s research questions, interview protocols, coding frameworks, and analytic approaches; leading one or more
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traffic flow theory with machine learning, and with that, the best of both worlds: theory and logic where necessary, data-driven where possible. This innovative new approach enables more efficient and
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geothermal-induction theory. The candidate will be employed at TU Delft, but will also carry out work together with KNMI (at De Bilt, NL), and will be embedded in the lively Geothermal Theme of TU Delft
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 5 hours ago
Organization National Energy Technology Laboratory (NETL) Reference Code NETL-PIP-2026-Lackey How to Apply A complete application consists of: An application, including academic history, work
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to learn about, (road) network traffic flow theory and simulation. You are interested in mentoring and supporting MSc and PhD students. You are a machine learning enthusiast (and realist). You love coding
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for research. Research experience in wireless communications, signal processing, information theory, or related areas. Preferred Qualifications: Research experience in machine learning for wireless