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interdisciplinary research centre of the University of Luxembourg. We conduct fundamental and translational research in the field of Systems Biology and Biomedicine – in the lab, in the clinic and in silico. We focus
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of Systems Biology and Biomedicine - in the lab, in the clinic and in silico. We focus on neurodegenerative processes and are especially interested in Alzheimer's and Parkinson's disease and their contributing
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Our collaborative group, composed of the MDM group at the University of Luxembourg, led by Prof. Letellier and the laboratory of Prof. Tejpar at the KU Leuven, investigates stromal–epithelial crosstalk in colorectal cancer initiation, progression and therapy resistance. Using state-of-the-art...
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Join a project at the intersection of the developmental cell biology of the oncogene RAS and drug target validation in the Cancer Cell Biology group under the supervision of Prof. Daniel Abankwa
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interdisciplinary research centre of the University of Luxembourg. We conduct fundamental and translational research in the field of Systems Biology and Biomedicine – in the lab, in the clinic and in silico. We focus
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Biology, Data Science, or a related field. Technical Proficiency: Strong command of R. Knowledge of Python is an asset. Multi-Omics Expertise: Proven experience in the analysis and integration of diverse
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of Systems Biology and Biomedicine - in the lab, in the clinic and in silico. We focus on neurodegenerative processes and are especially interested in Alzheimer's and Parkinson's disease and their contributing
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of Systems Biology and Biomedicine - in the lab, in the clinic and in silico. We focus on neurodegenerative processes and are especially interested in Alzheimer's and Parkinson's disease and their contributing
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research results in leading international conferences and journals in machine learning, computational biology, and AI for science The postdoc will work at the interface of machine learning, genomics, and
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learning, computational biology, and AI for science The postdoc will work at the interface of machine learning, genomics, and scientific computing, contributing both methodological innovation and