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Sciences (FHSE) at the University of Luxembourg brings together expertise from the humanities, linguistics, cognitive sciences, social and educational sciences. People from across 20 disciplines are working
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with a doctorate In-depth knowledge and experience in computer tomography and experimental imaging techniques, demonstrated by publications, scientific documentation, and active participation in
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, TensorFlow, Pandas), ideally combined with knowledge of data visualization or statistical analysis Knowledge of software development (e.g., Python, Matlab, Simapro), especially in combination with experience
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Postdoc (f/m/d) in Irradiation Tolerance of Additive Manufactured Ferritic/Martensitic Steels for...
at scientific conferences Your profile # Completed university studies (PhD) in the field of Materials Science, Materials Engineering, Physics, Nuclear Engineering or a comparable field # Sound knowledge in
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knowledge on multi-actor collaboration, networking and bridging, the applicant will conduct ecosystemic analysis and initiate interventions, measure synthesis, dead-ends and resistance and, in comparison with
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on genome bioinformatics/computational biology Excellent English language skills both written and spoken are required, Basic knowledge of German is desirable Desirable skills and qualifications Postdoctoral
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of chemicals in biological matrices. Knowledge in the field of human exposure to pollutants. Skills in Epidemiology and statistical treatment of data will be considered an asset. Significant record
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applications. Entry requirements To quality, the applicants must hold a PhD in computational biology or bioinformatics and have strong knowledge of RNA biology and biomedical sciences. Experience with sequencing
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Experience with basic molecular biology techniques (PCR, cloning etc.) Preferably knowledge in the characterization of microbial rhodopsins Skills in data analysis with Python Excellent organizational
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-solving and time management skills, ability to design and conduct independent research, learn new techniques, knowledge of the literature, critical interpretation of data and clear data presentation