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Field
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algorithms, architectures, and learning strategies that fundamentally challenge existing resource constraints in large-scale AI systems. Prototype, implement, and rigorously evaluate complex machine learning
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agentic AI, as well as AI standards to create trustworthy information resources. This includes the design of algorithms, tools, and process for knowledge workers to better organise knowledge bases and
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will be part of a research environment focusing on integrating multi-source satellite remote sensing data and developing novel algorithms to quantify agroecosystem variables for environmental
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manufacturer. Based on our publication (ACS Appl Nano Matter 2022, 10.1021/acsanm.2c03406) and our ongoing collaborative work, we have developed a new chemical assay coupled with a machine learning algorithm to
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on the physical layer design of wireless communication systems and explore enabling technologies for 6G and beyond wireless networks. Applicants must hold a PhD degree in electrical/electronics engineering
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agentic AI, as well as AI standards to create trustworthy information resources. This includes the design of algorithms, tools, and process for knowledge workers to better organise knowledge bases and
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to ensure that the developed notations and algorithms address the companies’ needs. More about the related project can be found here: https://innovationsfonden.dk/da/news-article/ai-skal-forudsige-og-forklare
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to algorithms, and cross-platform co-design across superconducting, neutral atom, and diamond-based systems, guided by quantitative resource estimates targeting DOE-priority scientific applications. Position
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passionate Postdoc (exceptional PhD candidates may also apply) to join our team and develop the next generation of intraoperative neurosurgical image guidance. Currently, neurosurgeons lack real-time
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giving academic presentations. *Trained as a theorist in either condensed matter (CM), atomic, molecular and optical (AMO) physics or in quantum information theory. *Interested in quantum algorithm and