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: Develop innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry
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:100% of the full-time weekly hours Tasks: The PhD student will be responsible for the modeling and simulation of 3D reconfigurable architectures e.g. based on emerging technologies (e.g. RFETs
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facilities, including advanced microscopy, controlled environment growth rooms, genomics, proteomics, and metabolomics platforms. • Opportunities to work across model and crop species, including Arabidopsis
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Job Description The Institute of Mechanical and Electrical Engineering at SDU invites applications for a 3-year PhD position focusing on CMOS implementation of State-Space Model-driven Spiking
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properties of catalysts together with statistical methods to derive predictive models for selective catalysis. In a data-driven approach, an initial set of reactions is analyzed and used to establish such a
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results accurately and draw meaningful conclusions to inform further research and process improvements. Background in modelling and simulation using simulation software. Background in Techno-economic
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immunology. For a recent list of topics that the Murrell lab works on, please see: https://scholar.google.com/citations?hl=en&user=I80vy5cAAAAJ&sortby=pubdate and https://staff.ki.se/people/benjamin-murrell
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of topics include algorithmic fairness in network analysis, developing network embedding frameworks for real-world network datasets or AI models based on agentic LLMs for simulating real-world network data
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acceptance of new products and business models. Immersive media technologies (IMT), such as Virtual Reality and Augmented Reality, may play an important role in exploring consumer behavior, and also represent
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, tasks have a continuous evolution, and the precedence graph becomes dynamic. There is an initial method proposed in the literature, where a static model is proposed, introducing two states of products