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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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requirements: PhD obtained less than seven years ago in Chemistry, Materials Science/Engineering, Chemical or Biomedical Engineering, Biotechnology, or a related field. Proven experience in electroanalysis and
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analyses, at bulk and single-cell resolutions. Good understanding of approaches for inferring gene regulatory networks and for predicting cell-cell interactions. Strong interest in adapting machine learning
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