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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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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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biology, or analytical workflows. Interest in single-cell analysis, cancer biology, and translational research. Basic level expertise in computational biology (e.g., bioinformatics, machine learning), with
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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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, optimization, control, game theory, and machine learning. Interdisciplinary by design: Work at the intersection of energy systems and markets, privacy and cybersecurity, forecasting, optimization, control, game
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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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Programming skills in Python, R, and/or GIS tools Highly valued: Background in LiDAR point-cloud analysis and vegetation structure analysis or habitat monitoring Experience applying AI or machine learning
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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