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to have a strong interest in data analysis, and medical research, along with relevant academic background and skills within medical image analysis and machine learning that will enable them to contribute
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schemes. Building ion trapping setup for Ca+ ions. Learning/operating fabrication and characterization equipment e.g. STM. Simulating fabrication methods. Collaboration with other groups at NQCP and
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Compression of quantum data under unreliable entanglement assistance Joint compression and error correction for robust communication in the quantum-classical internet Quantum embeddings for machine learning
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with expertise in digital signal processing methods, and machine learning methods for amplitude and phase noise characterization of optical frequency combs, recovery of dual-comb measurement signals and
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student will become part of a team at DTU with expertise in digital signal processing methods, and machine learning methods for amplitude and phase noise characterization of optical frequency combs
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Kontogianni. Our research explores how intelligent systems can perceive, understand, and interact with the 3D world. We develop new methods in computer vision, machine learning, and multimodal 3D
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behaviour. This will include developing and using state-of-the-art image recognition algorithms to create digital twin models as well as statistical and machine learning methods for analysing large-scale
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mass spectrometry and machine learning now allow us to unravel this “dark proteome.” This position aims to use state-of-the-art AI-guided proteomics and systems biology approaches to map protease
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Job Description If you are ambitious and interested in joining a supportive and dynamic research team working with Operations Research and Machine Learning on an important application look no
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(EoS), or machine learning approaches. Hands-on experience in extracting bioactive compounds from biomass. Strong collaboration skills and the ability to work effectively in interdisciplinary teams. A