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Field
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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printing, embedded sensing, and adaptive control, aiming to meet the unique challenges of automation in life science environments. Research tasks and qualifications The main purpose of this PhD position is
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within LTU’s AIC³ Lab (Automation, Industrial Computing, Communication, and Control Laboratory). Subject description The research subject focuses on an integrated development of network architectures
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Computations (DCC). The position is connected to two Horizon Europe projects, UPPRAISE and MEDUSA, as well as research activities within LTU’s AIC³ Lab (Automation, Industrial Computing, Communication, and
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systems has been an object of intensive research. While verification support for these languages does exist, it is limited in either the guarantees it can provide or in its level of automation. Lifting
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performed by human experts. The aim of this PhD project is to design innovative monitoring algorithms for complex dynamical systems to automate fault isolation with diagnostics performance guarantees. Current
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and radio-frequency (RF) integrated circuits (ICs) design automation for deep nanometer CMOS technologies. Deep nanometer technology nodes enable the design of highly integrated systems for consumer and
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(Mialyk et al. 2024) and improve the model’s ability to provide recurring updates of global crop water footprint accounts and automize key routines. Based on the resulting accounts, you will calculate water
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way. You will first work in our optical characterization lab, but then transfer these techniques to automated wafer testing tools. So, where is the scientific challenge in all this? The field of PICs is
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hosts about 110 researchs active in the field of materials, surfaces, processes and structures. The neuro group is based in FEMTO's Department of Automation and Robotics, which hosts about 80 researchers