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Field
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the testing of newly devel-oped materials and the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission
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Programme in Linguistics or Language Acquisition – a condition. Research focus on L2 or multilingual acquisition of grammar – a condition. Experience in the field is not required. Language skills Knowledge
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Organisation Job description Project and job description Our project will make use sensing technologies (hyperspectral cameras, NIR and Raman sensors), and an edge-compute AI pipeline to sort used
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of diverse, deformable textiles at cycle times below one second, while hyperspectral, NIR, Raman, and RGB sensors feed an edge-compute AI pipeline for real-time decision making that routes each item
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the ranking. However, STV method becomes considerably more complex with encrypted ballots. Our goal is to develop an algorithm/protocol to count encrypted ballot using the STV method. Our first point of
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Identifying and validating models for complex structures featuring nonlinearity remains a cutting-edge challenge in structural dynamics, with applications spanning civil structures, microelectronics
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, including patch-clamp electrophysiology, high-resolution imaging, chemogenetic manipulation of glial activity, and behavioral assays in rodent models Analyze and interpret complex datasets from
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experience Ph.D. – Doctoral Degree Programme in Linguistics or Second Language Acquisition is required. Focus on phonology and/or morphosyntax is an advantage. Experience in usage-based methodologies is an
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: Computational Modelling: Employing simulation tools (e.g., GEANT4, light transport) to explore novel metamaterial designs, predict performance, and optimise key parameters such as timing resolution, light yield
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manufacturing. As steel is highly recyclable its usage helps in creating a more sustainable world. For simulations of industrial processes concerning such complex materials one must typically rely on continuum