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data and deep learning methods to assess canopy cover, quality, carbon stocks, and ecosystem services. Mandatory requirements: PhD in areas related to forest resources, remote sensing, data science, or
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machine learning algorithms to support the traceability of the beef’s geographical origin. She/he will participate in all stages of the project, including planning and supervision of sample collection and
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to industry and developments with Deep Learning (DL), Computer Vision (CV), Transformers, Large Language Models (LLMs), Natural Language Processing (NLP). Mandatory requirements • Bachelor's degree
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position within a Research Infrastructure? No Offer Description Activities and context: The fellow will develop machine-learning interatomic potentials (MLPs), trained on density functional theory-DFT data
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; • Familiarity with statistical analyses and modeling as well as machine learning approaches, supported with strong skills in computational optimization of methods; • Experience working with large-scale datasets
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networks, from a machine learning and information theory perspective. This basic research project has strong translational potential and aims to elucidate how immune function is altered during sepsis, with
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in tropical regions; analyze links between macrofauna and soil carbon; build/validate scoring algorithms using machine learning/cumulative functions. Outputs – Lead scientific, technical, and policy
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. Mandatory requirements: Demonstrated knowledge of machine learning techniques and programming, with extensive experience in data analysis using Python and R. PhD in Economics, with expertise in health
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scientific programming language (Python, Fortran, or C); familiarity with inversion methods or machine learning; experience with high-performance computing (HPC) environments. Desirable requirements Experience