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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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. The project is led by Heiko Schütt and will employ one PostDoc and one PhD student. About the role... You will develop new Bayesian methods to compare deep neural network and other artificial representations
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, including the development and use of 3D models and the study of extracellular vesicles Analyze high-throughput data sets using R or similar software Present research findings in meetings Document performed
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science, pharmacology, biomedicine or related fields Demonstrated expertise in computational modelling Experience with molecular dynamics Interest in interdisciplinary science Experience in working on High-Performance
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neurons Perform functional genomics experiments (also at single cell level) and contribute to their computational analysis Communicate research results in international conferences and journals Work
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patient clusters and digital phenotypes, leveraging machine learning approaches to identify individuals at high CV risk based on clinical and biochemical markers, immune markers, digital health data (e.g
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interstellar ices to the early stages of planetary systems. The successful candidate will perform multidimensional gas chromatographic analyses of astrophysical-relevant samples including interstellar ice
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and educational issues with the common goal of contributing to an inclusive, open and resourceful society. Your role We are looking for a doctoral candidate with a strong computational, engineering
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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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for neurodegenerative diseases. Outcomes could be diagnoses of mild cognitive impairment, dementia, and/or Parkinson's Disease, or markers of brain structure and functioning, depending on the dataset