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inverse problems. The team aims at developing Bayesian computational methods for such (ill-posed) inverse problems and aims both at increasing their validity and at reducing their computational cost. In
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foster students' ability to critically apply advanced quantitative and computational methods to real-world economic and policy challenges. In particular, the following requirements apply: PhD in Economics
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/ Deep Learning (particularly Computer Vision or 3D perception) Verification & Validation (V&V) of advanced algorithms, software or systems Formal Methods Safety Engineering / Safety-Critical Systems
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of the research are: AI-assisted Bond Issuance, Causal Methods for Enhanced Market Intelligence, Automated Market Research Summaries via RAG-enabled AI. Successful PhD candidates will extensively explore and
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processing, embedded systems, machine learning, and networked communication. Each PhD position corresponds to a dedicated research topic within the consortium. All doctoral researchers will benefit from joint
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ensure timely and high-quality outputs Publish in top-tier outlets, contributing to the strategic goals of SnT and FINATRAX Supervise and mentor PhD, BSc and MSc students Engage in teaching activities
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design studies, and investigating human-computer interaction using various methods. In addition, the candidate will have the opportunity to contribute to the organisation of networking events, such as
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datasets for single nucleotide variants, structural variants, and tandem repeats relevant to FTLD Use cutting-edge bioinformatics software and methods, or develop novel tools when appropriate
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more information, please visit our website: www.uni.lu/snt-en/research-groups/finatrax/ The candidate will be enrolled in the PhD program in Computer Science and Computer Engineering with specialisation
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reliability (e.g., through formal methods) Experience in software development will be seen as an advantage. Preference may be given to candidates who develop themes complementary to research currently being