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Field
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microcontrollers (e.g., Arduino). Analysis and processing of biological signals Development of algorithms in Python for real-time signal processing. Extraction and analysis of multi-signal cross-frequency coupling
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connections, measurement of relevant electrical signals, designing and building: water cooling system, gate drives, signal to power interface cards, designing magnetic transformers and inductors including
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approaches to host/pathogen interactions, cell signalling and comparative immunology of zoonotic infections. The applicant should have a track record of research in computational biology with experience with
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analysis and visualization, signal processing, and ideally machine learning. • Working knowledge of Distributed Acoustic Sensing (DAS) and its applications in seismology (appreciated). • Aptitude
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to contribute to a pioneering project at the intersection of natural language processing, historical linguistics, and computational humanities. The postholder will lead the computational stream of the project
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the electrical contacts and magnetic field to influence the fault current interruption process. The PDRA will contribute mainly to the arc modelling part of the project and play an active role in organising and
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. Demonstrated autonomy 2 to 5 years' experience in signal processing and observational seismology, ideally applied to environmental seismology. Where to apply Website https://www.aplitrak.com/?adid
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models, handling of biological samples, molecular and cell biology techniques, omics data analysis, and scientific writing. LanguagesENGLISHLevelGood Additional Information Eligibility criteria Indicate
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probability theory. A good starting point to get familiar with the topic is the paper ‘Sharp threshold for the ballisticity of the random walk on the exclusion process’ ( https://arxiv.org/abs/2409.02096 in
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: digital signal processing, statistics, multimodal processing, FAIR data management, music theory, or musicology Personal skills Strong ability to work purposefully, systematically, and independently Time