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on Data Representation and Compression, which focuses on developing efficient methods for representing, processing, and reliably transmitting both classical and quantum data under strict timing constraints
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leading universities and non-academic partners will train 14 doctoral candidates in cutting-edge methods, theory, and transferable skills. More information may be found on the ENERPOL website: https
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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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and physical principles of negative ion mode LC-MS/MS-based peptide analysis. The project combines method development and mechanistic exploration with downstream applications in proteome
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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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, TensorFlow) and protein language models. Experience with programming (Python preferred) for bioinformatics or data science applications. Exposure to degradomics methods or post-translational modification
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optimization techniques. Implement computationally efficient optimization methods to solve the developed models for realistic problems from practice. Conduct experiments and benchmarks and report results. Write
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, and device engineering. Responsibilities and qualifications Your overall focus will be to develop and apply nanofabrication methods for smoothly varying lithographic structures in van der Waals