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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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mathematical models and refine parameters for single-cell, genomics, and metabolomics data analyses. While primarily focused on computational work, they may elect to acquire hands-on experience with
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skills in data analysis, machine learning, as well as in mathematical and computational modelling? You will have the opportunity to investigate innovative solutions using machine learning algorithms and
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pediatric cancers. In prior work, we have developed tools and methods to map the cellular diversity of pediatric tumors by adapting single-cell sequencing techniques to archival frozen tumors. In that work
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; collaborating closely with molecular biologists, computational modelers, and clinical and preclinical research partners; documenting results clearly and contributing to publications and presentations; and
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students on research related work and provide guidance to PhD students where appropriate to the discipline Contribute to developing new models, techniques and methods Undertake management/administration
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/MS for example) Advanced knowledge in research design and methods (running extensive experimental studies) Familiarity with practices of transparency and open science Strong interpersonal and
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Postdoctoral Research Associate - Training in Pediatric Cancer Survivorship Outcomes and Interventio
; a top-ranked scientific environment; and superb benefits, mentoring, and professional development. Positions are available in diverse research areas, including epidemiology, genetics, computational
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data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms to understand