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systems at various scales, for example using ab initio electronic structure methods like density-functional theory, developing interatomic potentials with various methodologies including machine learning
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language models from LLMs. Demonstrated publication record in the machine learning and AI field. Excellent programming and computer science skills. Preferred Qualification: Doctoral degree in electrical
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optimisation. State-of-the-art digital models and AI tools that incorporate machine learning could enable predictions of the dry fibre forming that are subsequently used as input into the RTM process model
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databases. Design, implementation, and testing of deep learning and AI algorithms for processing tabular, genomic and temporal data. Where to apply Website https://www.uam.es/uam/investigacion/ofertas-empleo
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role in defining system requirements and developing a robust AI framework to model and anticipate opponent behaviours and beliefs, leveraging state-of-the-art methods in machine learning and
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genome-resolved multi-Omics methods, statistical/metabolic modeling, and machine learning. The postdoc will apply these approaches to generate a systems-level understanding of microbiomes including
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promise and peril of hybrid intelligence—humans and machines working and learning together. Our mission is to establish an internationally leading interdisciplinary hub that advances foundational research
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ML models and Python programming. Work Objectives: The main objective of this position is to develop, implement, and validate advanced machine learning methodologies within the scope of the project
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engineering/M2) to have a solid background in applied mathematics, Machine/Deep Learning, in particular generative models (diffusion models, flow matching), as well as in statistical signal/image processing and
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research background in or research experience with one or more of the following topics: Natural language processing & language modeling Machine learning & representation learning Interpretability and