132 processor-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"NIST" PhD positions
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PhD Scholarship Opportunity: Secure Quantum Computation on Shared Quantum Processors Job No.: 699200 Location: Clayton campus Melbourne Employment Type: Full-time Duration: The scholarship may be
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PhD Studentship: Efficient Long-Horizon Task Execution in Physical AI (deep learning, computer vision, robotics) Number of awards: 1 Award information: Fully funded PhD studentship covering Home
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, engineering, mathematics, physics or related field; strong programming skills; interest in ML, computer vision, robotics, embodied AI or autonomous systems; motivation for independent research and high-quality
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for using AI to develop social engineering attempts. This project combines human subject research of learning and decision making, Human-Computer Interaction, and the advancement in AI methods
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LanguagesENGLISHLevelGood Additional Information Additional comments The full text of the call of proposal can be found at the following link: https://unipv.patisweb.it/page_tg/51302/concorsi-per - ricercatori.html
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Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Electrical and Computer Engineering programme. The position
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will develop efficient quantum compiler methods to bridge the gap between quantum algorithms and their execution on quantum processors. The project will develop innovative quantum software components and
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Building a quantum computer that can solve real-world problems will require the integration of many highly coherent qubits. Hole-spin qubits in planar germanium quantum wells are among the most
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to this. The gathered data will provide new insight in the exact timing and variability in past landscape changes. Your results will furthermore contribute to computer simulations of coastal dynamics in a parallel PhD
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. Depending on the candidate's background and research direction, approaches may include computer vision, representation learning, graph neural networks, multimodal learning and scientific machine learning. A