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, materials science, applied mathematics, or a closely related discipline. The project focuses on failure and fracture phenomena. Knowledge of condensed matter or statistical physics, as well as modelling
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analysis with practically motivated case studies, offering a strong foundation for researchers interested in advancing the mathematical understanding of geometric deep learning. Your Qualifications PhD
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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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Ph.D. or equivalent degree in mathematics, physics, computer science, bioinformatics, or a related field Experience in developing deep learning models Ideally, prior experience in analyzing biological
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master's degree (or equivalent) and PhD in Mathematics/Statistics/Data Science Research expertise in the analysis of complex systems Familiar with network analysis, concepts of resilience (e.g. adaptive
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closely with the Department of Mathematics at Aarhus University. In addition, your organic chemistry skills will be instrumental in developing antioxidant artificial cells. These interdisciplinary projects
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with a PhD in a quantitative field (computational biology, bioinformatics, systems biology, genetics/genomics, statistics, mathematics, computer science, or related fields) are encouraged to apply
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outstanding postdoctoral researchers in the fields of Physics, Chemistry, Mathematics, Computer Science, Earth Science and the Life Sciences. This collaborative program offers a comprehensive four-year position
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Job description: The University of Vienna is a cosmopolitan hub for more than 10,000 employees, of whom around 7,500 work in research and teaching. They want to do research and teach at a place that suits their ideas and work together towards answering the big questions of the future. You also...
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PhD Degree in a relevant scientific field (e.g. computer science, data science, mathematics, engineering, or related); Strong understanding of generative models (e.g., VAEs, GANs, transformer-based