52 phd-studenship-in-computer-vision-and-machine-learning PhD positions at University of Luxembourg
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website: www.uni.lu/snt-en/research-groups/finatrax/ The selected candidate will be enrolled in the PhD program in Computer Science and Computer Engineering with specialisation in Information Systems (IS
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framework to bridge this gap and enable organizations to confidently deploy secure GenAI solutions by evaluating the machine-learning models intrinsically, identifying components of an AI pipeline and their
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You will join the recently established Chemical and Molecular Neurobiology group led by Associate Prof. Ivana Nikić-Spiegel at the LCSB. Our research combines innovative approaches in protein engineering, microscopy, and chemical biology to investigate neuroimmune interactions and mechanisms...
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cybersecurity allowing thus to validate and receive feedback from on-the-field cybersecurity practitioners. The PhD candidate will investigate how AI and above all Generative AI (GenAI) can be leveraged
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proof-of-concept software tools Machine learning is a plus Strong analytical and programming skills are required (Python, Matlab, and C/C++). Prior proven experience in data-driven innovation projects is
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. For more information, please visit our website: www.uni.lu/snt-en/research-groups/finatrax/ Candidates will be enrolled in the PhD program in Computer Science and Computer Engineering with
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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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conducts research on the application and the impact of digital technologies like DLT/Blockchain, Digital Identities, and Machine Learning/AI 5G on organisations from both the private and public sectors
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conducts research on the application and the impact of digital technologies like DLT/Blockchain, Digital Identities, Machine Learning/AI, GenAI, and IoT/5G on organisations from both the private and public
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photopolymerization of the precursor. The practical work will be complemented by fluid mechanics computer simulations, including solutions employing machine learning, and theoretical analysis using Leslie-Ericksen