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transcriptomics data analysis. Experience in quantitative image analysis, computer vision, or digital pathology. A strong background in cancer biology or immunology. Experience with machine learning, deep learning
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Leonardo. The successful candidate will play a crucial role in developing and optimizing machine learning workflows for large-scale environmental data analysis, contributing to the creation of robust and
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9 Oct 2025 Job Information Organisation/Company Universitat Pompeu Fabra - Department / School of Engineering Department Engineering Research Field Engineering » Computer engineering Researcher
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Postdoctoral researcher in marine ecosystems modelling for the Marine and Continental Waters Program
of machine learning and AI algorithms and methods. Knowledge of species distribution models. Catalan and Spanish are valued LanguagesENGLISHLevelGood Research FieldOtherYears of Research Experience1 - 4
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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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Experience in machine learning techniques Postdoc 3: Experience in the computation and analysis of hydrodynamic cosmological simulations of galaxy formation and evolution Experience in simulations
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“CAR-T Cell Immunotherapy against Stromal Antigens for the Treatment of PDAC (CAR4PDAC)” under the direction of Drs. Juan José Lasarte and Felipe Prosper. The project is funded by the AECC Foundation
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of both official languages of Catalonia. Advanced English level. Advanced computer skills (Excel, Word, PowerPoint, etc.) Skills will be valued Strong knowledge of molecular biology applied
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build using molecular dynamics, the MACE foundation models and density functional theory. Main Tasks and responsibilities: AI4LSQUANT aims to accelerate quantum modelling by learning fast, accurate
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documentation. Contribution to applications to HPC centres (with a focus on EuroHPC machines) in order to secure the resources needed for architecture-dependent code development, optimised deployment and