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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees
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the EU’s ambitious AI Factories initiative. Learn more: https://mimer-ai.eu/about-mimer/ , https://www.naiss.se , https://eurohpc-ju.europa.eu/ai-factories_en The position In this role your responsibility is
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/hacohen19a.pdf [4] Roh et al., FairBatch: Batch Selection for Model Fairness — https://arxiv.org/pdf/2012.01696 [5] Ren et al., Learning to Reweight Examples for Robust Deep Learning — https://arxiv.org/pdf
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and machine learning based analyses including predictive modeling and real world evidence generation. Basic Qualifications: MS in computer science, biostatistics, biomedical informatics or related field
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 5 days ago
. The candidate(s) may also be required to apply data fitting algorithms/machine learning algorithms to link models to biological data from the literature. The project integrates elements from dynamical systems
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. The postdoctoral fellow will lead efforts to develop novel machine learning models for integrating omics datasets (e.g., genomic, transcriptomic, epigenomic, proteomic, metabolomic) with relevant molecular pathways
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Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling, machine-learning model development, structural sensing and health monitoring, conducting physical
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requirements and focusing on data-value maximisation. This project will utilise innovative machine learning methods and tools from process systems engineering to simultaneously optimise product quality and the
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, implementation, and analysis of machine learning models for computer vision tasks (40%). Analysis of natural scene statistics in aquatic and terrestrial environments (40%). Design of models to learn texture
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-physics modelling of power electronic systems and components, with special focus of magnetic components, Incorporating physics-driven machine learning approaches in power electronics design, Incorporating