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Field
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+ transients, voltage, contraction). To integrate our findings and generate new predictions and hypotheses, we aim to use multimodal deep learning and new systems analysis frameworks. Research area The
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/deep learning with GWAS/sequencing data and other types of omic data, as in Mendelian randomization and TWAS. In addition to new methods development and evaluations, the job responsibilities include
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. Familiarity with statistical modelling, machine learning and deep-learning Additional information: We offer: 🌐The opportunity to work with our state-of-the-art HPC infrastructure and to join a vibrant network
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Machine/Deep learning and classification Knowledge of the Linux operating system for using a computing cluster Interest in transdisciplinarity and teamwork Autonomy and scientific rigor Website
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regulated training activities and contribute to continuous training activities. Conduct research that allows the development of new AI methodologies based on deep learning that allow for assisting musical
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-- Deep learning for nuclear physics -- Effective field theory for nuclear structure -- Hard Probes of Quark-Gluon Plasma -- Hot and cold lattice QCD -- Physics in electron-ion collisions -- Relativistic
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to develop and implement machine learning/deep learning tools for personalized medicine in cancer by exploiting electronic medical records and medical images in relation to cancer diagnosis and the
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the LAMP group at the Computer Vision Center (CVC), in Barcelona, Spain. The position is for 2-3 years and linked to the project “Foundations for Adaptive and Generalizable Deep Learning” (EXPLORA
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, machine learning and deep learning. The project Motivation: Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology. Achieving
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to explore new analysis methods using deep learning. Depending on the interest, it is also possible to be involved in the wet lab. Professor Tiwari also leads the Center for Integrated Multiomics in Precision