98 high-performance-quantum-computing-"https:"-"https:"-"https:"-"https:"-"https:" positions at University of Luxembourg
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. The group consists of doctoral and post-doctoral researchers from diverse backgrounds. For more information, please visit our website: https://wwwen.uni.lu/snt/research/finatrax/projects Successful candidate
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builds on an established platform and focuses on measurable performance gains, traceable calibration, and demonstrated stability under realistic operating conditions, with a clear route to industrial
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datasets Collaborating closely with data providers, clinicians, and technical teams to ensure high-quality data integration, validation, and analysis workflows Supporting documentation, reproducibility, and
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activities, including the design and implementation of novel methods, experimental evaluation, and dissemination of results through high-quality scientific publications. Beyond technical contributions, you are
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to perform the following tasks: Carry out the aforementioned research with national and international collaborators, e.g., INRIA Rennes, University of Zaragoza, TU Munich or University College London
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conducts research on the application and the impact of emerging technologies like DLT/Blockchain, GenAI, Natural Language Processing, Machine Learning, Human-Computer Interaction, and IoT/5G on organisations
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, particularly those affected by REM sleep behavior disorder (RBD), a high-risk group for developing PD. Using cutting-edge technologies including iPSC-based dopaminergic neuron modeling, single-cell
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peptides Perform and support experimental studies across the METAMIC project, including notably metagenomic sequencing of field study samples (from clinical or environmental use cases) Application
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technologies that have a positive impact on society. For further details, please visit our website: https://www.uni.lu/snt-en/research-groups/finatrax/ The candidate will support project partnerships with
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ribosomes and their genetic manipulation in dopaminergic neurons from various PD models. The aim is to use these findings to identify translationally aberrant protein products that have a high propensity to