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implement innovative architectures for real-time detection and control of laser processes. This interdisciplinary role combines artificial intelligence and machine learning with the physics of laser–matter
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responsibilities may include: Development or analysis of novel Machine Learning algorithms for engineering design applications, such as Inverse Design, Surrogate Modeling, or generative modeling. Collaborating with
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Metagenomics, meta-transcriptomics and metabolomics data analysis and familiarity with gut microbiome research. Machine learning for genomics (representation learning, generative models, causal inference). Multi
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plasma models (such as magnetized Vlasov–Poisson and Vlasov–Maxwell systems). - The position includes teaching responsibilities, such as organising a course and assisting with exam grading
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modelling and AI/ML for the quality monitoring/control, at the end offering to the society novel nanostructured materials, their shape-forming and integration into devices. Your tasks We are seeking a highly
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cryo-EM workflow (sample/grid preparation, data acquisition, data processing, model building and interpretation). The writing of scientific papers and active participation in internal meetings and in