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academic and professional qualifications Proven research experience in the field of modelling and analysis of biological networks Solid foundation in mathematics and algorithmic design Strong programming
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Postdoc (f/m/d): Machine Learning for Materials Modeling / Completed university studies (PhD) in ...
Area of research: Scientific / postdoctoral posts Starting date: 01.07.2025 Job description: Postdoc (f/m/d): Machine Learning for Materials Modeling With cutting-edge research in the fields
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on the influence of Alzheimer’s disease and aging on changes in cognitive functions in humans. The project combines cutting-edge technologies from genetics, proteomics and statistical modeling to understand
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(EM) forward modeling, inversion, and applied EM studies. Within this team, the candidate will be responsible for the numerical development of inversion codes for both frequency-domain (FEM) and
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modelling of complex plasma processes, contributing significantly to fields such as fusion reactor design, material deposition technologies, and space propulsion systems. The project brings together a
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Postdoc position: mechanisms of autoimmunity & autoinflammation in inborn errors of immunity (m/f/d)
erythematosus (SLE). This project will integrate biochemical, immunological, and imaging techniques, along with co-culture of patient-derived organoids and model organisms combined with various –omics approaches
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found on hpc.uni.lu . The activities include classical HPC applications such as simulation and modeling, but also artificial intelligence and machine learning, bridging computational science, with data
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experience on numerical modelling of aerodynamic and/or hydrodynamic loading on structures. The applicants shall demonstrate the following experiences: Teaching at undergraduate and graduate levels
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with applications in Earth Sciences Experience in numerical solution of partial differential equations Experience with the geodynamic modelling software ASPECT is of advantage but not a strict
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environment using models and data assimilation. We study the fundamental processes in the near-Earth environment and focus on understanding fundamental processes responsible for the evolution of space radiation