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in functional genomics methods (e.g., single-cell and bulk RNA-seq, ATAC-seq and ChIP-seq) and computational data analysis is considered highly advantageous. Selected references: Jakobsen et al., 2024
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neural networks are large black boxes, we need both automatic methods to analyze such systems and human-interpretable certificates of the analysis results. We will devise automatic and sound methods
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Development/upgrading solid oxide cell test setups for advanced electrochemical characterisation, including development of metal test houses Development of dedicated circuitry and analysis software
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verification and formal verification. runtime analysis and reconfiguration of in-vehicle TSN networks. Co-supervise students. Optionally contribute to teaching. Required qualifications: Hardware design in a
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exploit such biomass when scaled up and integrated into an industrial setting. This analysis requires the combined application of qualitative and quantitative approaches to identify and describe future
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results with AI models and system simulations to create a digital twin of the PtX process for predictive optimization and scenario analysis. Funding This PhD position is generously funded through the Villum
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), robotics and computing, construction production processes, and life cycle and sustainability analysis (LCA). The successful candidate will be responsible for conducting cutting-edge research in the field
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. Training in state-of-the-art technologies and data analysis as well as research management, oral and written communication. Participation to international conferences to facilitate successful integration
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. We are looking for you if you have a background and interest in programming languages and formal methods --- e.g., has taken courses in program analysis, verification, compiler construction and/or
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failure analysis using advanced finite element models and simulation techniques. This is enabled by digital and sensor technologies such as artificial intelligence, computer vision, drones, and robotics