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prefer to receive applications online. We will update you about your application at various points throughout the selection process, via automated emails from our e-recruitment system. Please check your
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at interview. Submitting your application We prefer to receive applications online. We will update you about your application at various points throughout the selection process, via automated emails from our e
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throughout the selection process, via automated emails from our e-recruitment system. Please check your spam/junk folder periodically to make sure you have not missed any of our updates. What to Submit All
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: Leveraging LLM / VLMs for interdisciplinary problems, such as: AI-driven scientific discovery, automating hypothesis generation in finance / natural sciences / physical sciences, enhancing collaborative
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process, via automated emails from our e-recruitment system. Please check your spam/junk folder periodically to make sure you have not missed any of our updates. What to Submit All applicants are asked
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relevance. This position will be anchored at Communication, Control & Automation Section at ECE and will be supervised by Professor Qi Zhang, whose research focuses on Edge Intelligence, Goal-oriented
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, transport options and CO2 sources, as well as data on local regulation, (market ready) technologies and associated costs. The aim is to develop a (semi-)automated data acquisition workflow that will allow
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include 1) developing automated, precise, robust, intensified, modular, and electrified (A-PRIME) water desalination technologies to support a circular water economy; 2) optimizing the coordinated operation
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of machine learning methods for assurance on hardware devices like PCBs and ICs as well as on the development of automated hardware reverse engineering approaches at the IC and PCB levels for design recovery
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fields include (but are not limited to) Computer Science, statistics, mathematics, automation, informatics, and Engineering. • Experience in deep learning, machine learning and medical imaging processing