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deal with numerical and/or categorical data [e.g. Klassen et al., 2018], textual data [e.g Assael et al., 2022], images [e.g. Horache et al., 2021 and geospatial data [Ramazzotti, 2020]. Applications
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of programming, learning theory, parallel algorithms or quantum computing Research publications in theoretical computer science conferences and journals Experience in teaching Computer Science topics
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and and experience in computational methods applied to structural biology. A strong publication track record.
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of the moving sources, and directionality of the DAS measurements, make the use of machine learning techniques very appealing. The doctoral student will propose deep learning methods for source separation of DAS
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, dynamic and innovative researcher to integrate our community. The ideal candidate will possess deep expertise in the application of cutting edge computational methods to understand the brain mechanisms
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PhD degree in Computer Science, Physics or a related field Experience with parallel programming models Strong programming skills in C/C++ and/or Python Knowledge of distributed memory programming with
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the boundaries of cellular reprogramming by introducing scalable computational methods that streamline the discovery of reprogramming targets and control strategies. A key innovation of EdgeCR is its
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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, copyrighted, or biased. By studying brain data recordings and building computational models that mimic real populations of neurons, the project aims to uncover active unlearning: how the brain learns
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well as computational modeling. The development and numerical implementation of novel methods has become a key issue in modern oncology, both in terms of understanding the biology of cancers and for medical oncology