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experimental, pilot-scale, or high-fidelity simulation data into model calibration and validation workflows Design and run numerical simulations of multiphase flow systems and reactors Quantify model uncertainty
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management. Data from case studies (inspections, monitoring, and experimental tests) are used for model updating, calibration of safety formats, and prediction of future performance and remaining service life
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: Metallurgy, Process physics modelling, and Process development and control. Our main aim is to fundamentally study and improve our understanding of welding and welding-based additive manufacturing processes
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anaerobic culturing techniques (e.g. anaerobic chamber, bioreactor) and analysis of 16S sequencing data. Furthermore, practical experience in working with mouse models is required. Other requirements
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of cybersecurity and AI, i.e., attacks and defenses leveraging AI solutions, or attacks and defenses within AI solutions (e.g., backdooring, model poisoning, membership inference), cybersecurity of generative AI
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mathematical modelling and machine learning both using simulated and real data. Work duties The main duties involved in a post-doctoral position is to conduct research. Teaching may also be included, but up
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acoustic monitoring through research in dynamic noise mapping. You will develop real-time methods for road traffic noise assessment, including AI-based traffic classification and acoustic modeling of mixed
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such as spectrally controlled reflectivity and transmission, down conversion, light trapping and light extraction, nonlinear effects, and solar cells. The work involves electromagnetic modeling
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). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep
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imaging, computer vision, and predictive modelling. The postdoc will further develop an existing rumen‑fill scoring algorithm into a functional prototype and pilot the technology for longitudinal monitoring