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of knowledge-driven models, leveraging Bayesian statistics and causal inference for calibrated uncertainty, distribution-shift detection, and safety guarantees. You will be will working within the Center
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of knowledge-driven models, leveraging Bayesian statistics and causal inference for calibrated uncertainty, distribution-shift detection, and safety guarantees. You will be will working within the Center
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the application of rock physics models, Bayesian inversion methods, and machine learning algorithms in the electromagnetic context. Qualifications and personal qualities: Applicants must hold a master’s degree (or
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will be adapted to the candidate’s background and the evolving needs of the center. Possible directions include the application of rock physics models, Bayesian inversion methods, and machine learning
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and science, as well as PhD education in computer technology. The Mohns Center for Innovation and Regional Development researches innovation and offers master's education in innovation and
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University of Pisa. The PhD Fellow will be affiliated with the Computer Networks Research Group (ComNet) at the department. The proposed PhD project aims to advance the field of 6G vehicular networks by
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Design, Human–Computer Interaction (HCI), or Human-Centered Computing, with Human–AI Interaction as a central focus. If you are in the final phase of your doctorate and can document that the thesis has
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COMAP analysis pipeline, including the analysis of raw time-ordered data, mapmaking, and power spectrum estimation. The candidate will work on all aspects of the data analysis of the COMAP-Pathfinder and
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. The PhD Fellow will be affiliated with the Computer Networks Research Group (ComNet) at the department. The proposed PhD project aims to advance the field of 6G vehicular networks by harnessing the AI
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degree in Human-AI Interaction, or a closely related field, such as Learning Technologies, AI in education, Interaction Design, Human–Computer Interaction (HCI), or Human-Centered Computing, with Human–AI