12 learning-"https:" "https:" "https:" "https:" "https:" "https:" "https:" ";" Postdoctoral positions in Brazil
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, and machine learning; and the development of ecological indicators for environmental monitoring in the Campos and Santos Basins, Brazil. Mandatory requirements Applications from people with training in
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-doctoral fellowship in person in São Paulo, Brazil. Fluency in English. Basic knowledge of statistics and willingness to learn how to conduct systematic reviews and meta-analyses. Previous experience in
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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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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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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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Experience in plant breeding for resistance to biotic and/or abiotic stresses, programming, machine learning, and genomic data analysis. Desirable requirements 1. Cost-effectiveness assessment: rapid
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chromatography. The candidate will prepare samples for immunopeptidomics by liquid chromatography coupled with mass spectrometry tandem (LC-MS/MS), analyze large datasets, and develop scripts and machine learning
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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