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At the Faculty of Engineering and Science, Department of Materials and Production a position as PhD stipend in Machine Learning Assisted Terahertz Spectroscopy, within the doctoral programme
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-classical internet Quantum embeddings for machine learning Networked quantum sensing supported by distributed classical communication Prospective applicants to this PhD proposal should have the following
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converging operational research, machine learning, and decision-making methodologies. The ultimate goal is to create real-time autonomous systems that are not only trustworthy but also adaptive in the face of
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Professorhip grant, which you can learn more about here: https://www.cnap.hst.aau.dk/lundbeck-professorship As a PhD fellow your tasks include: Conduct research under the supervision of senior CNAP staff members
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activities of the IKE research group, but will, as part of the GreenTraC-project, also join a cross-organisational and interdisciplinary team working on developing and implementing machine learning and natural
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internet Quantum embeddings for machine learning Networked quantum sensing supported by distributed classical communication Prospective applicants to this PhD proposal should have the following
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with sensor data processing, AI, machine learning, and software development. As the position covers a broad scope– from sensor data acquisition to AI model development– we are open to considering
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developing the skillset on underwater perception technologies within topics such as: Qualifications: A master's degree in computer vision, computer science, robotics, electrical engineering, or a related field
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with 15 researchers at various career levels with a high expertise within molecular methods development, bioinformatics and machine learning. Furthermore, the position will be anchored in the Center
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candidate will be part of the Albertsen lab with 15 researchers at various career levels with a high expertise within molecular methods development, bioinformatics and machine learning. Furthermore