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tasks The scope of the PhD position(s) fall within the areas of novel low-complexity neural network architectures, generative audio techniques, and the integration of large language and speech foundation
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fundamental and applied research within communication, networks, control systems, AI, sound, cyber security, and robotics. The department plays an active role in transferring inventions and results
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complex interaction patterns that may carry important biological information. By integrating deep learning, genome-wide simulations, functional genomics, and large-scale biobank data, AI:GENOMIX aims
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challenge: how to evolve classical communication networks to support both traditional data and the unique requirements of quantum information systems (https://www.classique.aau.dk). CLASSIQUE will address a
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. Furthermore, the PhD candidate will engineer complex genotypes and perform adaptive laboratory evolution experiments. The candidate will work in an international team and is expected to contribute creatively
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virtual screening, generative machine learning (ML) models, and automated reaction exploration to gain insights into complex chemical systems. This is an opportunity to work at the forefront
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on constrained platforms using techniques such as model compression, quantization, and hardware-aware neural network design. Investigating mechanisms that protect the integrity and reliability of deployed AI
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distribution infrastructures, and heterogeneous networks and storage environments based on Cloud based services, CDNs and Edge Computing. This complex chain of delivery has negative implications
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lightweight AI models suitable for real-time execution on constrained platforms using techniques such as model compression, quantization, and hardware-aware neural network design. Investigating mechanisms
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problem-based learning model. The department leverages its unique research infrastructure and lab facilities to conduct world-leading fundamental and applied research within communication, networks, control