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to optimization problems with possible topics covering: Variational quantum algorithms for optimization Quantum annealing Quantum inspired optimization Quantum machine learning with a special emphasis on classical
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Multi-omics data integration and workflow improvement Development and application of machine learning-based algorithms for the identification of antibiotics-associated proteins and antimicrobial
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application of machine learning-based algorithms for the identification of antibiotics-associated proteins and antimicrobial peptides Perform and support experimental studies across the METAMIC project
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various international initiatives and engaged with different stakeholders. The candidate is expected to primarily but not exclusively deploy qualitative IS methods, with specific focus on case study
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for the environments they manage and their level of expertise because actions may be from different types and/or at different level (technical vs. organizational for example). This requires a solution to provide
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, emission reduction, and social acceptability. The candidate will be involved in various (inter-)national initiatives and engaged with different political decision makers. In addition, the candidate will have
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consolidated data model and advanced analytics to facilitate strategic decisions with relevant stakeholders. The candidate will be involved in various (inter-)national initiatives and engaged with different
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consolidated data model and advanced analytics to facilitate strategic decisions with relevant stakeholders. The candidate will be involved in various (inter-)national initiatives and engaged with different
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datasets such as the Luxembourg Parkinson’s Study and different prodromal cohorts for neurodegenerative diseases are ready for analysis. The doctoral researcher will: Conduct research that will compose a PhD
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-chemical-physics/ ) led by Prof. Alexandre Tkachenko at the Department of Physics and Material Science, we are looking for a PhD candidate to perform molecular dynamics simulations of different genetic