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crane. The successful candidate will build reproducible machine learning pipelines, integrate detections into spatial ecological models, and generate conservation-relevant outputs for regional
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, the individual will: 1) Analyze acoustic data with existing software (Echoview, R, Python). 2) Collaborate with USGS scientists on field data collections. 3) Investigate the spatial patterns of fish and
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crane. The successful candidate will build reproducible machine learning pipelines, integrate detections into spatial ecological models, and generate conservation-relevant outputs for regional partners
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of crop-switching, environmental resilience, and agrifood sustainability. The candidate will develop and apply spatial analysis, remote sensing, and modeling to evaluate the technical, environmental, and
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will lead research at the intersection of crop-switching, environmental resilience, and agrifood sustainability. The candidate will develop and apply spatial analysis, remote sensing, and modeling
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. In addition to furthering systematic thinking by separating problem formulation from solution concepts, integrating models from different disciplines, operating at different time/spatial scales
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research in genomics, whole-genome and exome sequencing, single-cell RNA, miRNomics, spatial transcriptomics, and mass spectrometry data. The successful candidate will work on two funded projects to identify
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research in genomics, whole-genome and exome sequencing, single-cell RNA, miRNomics, spatial transcriptomics, and mass spectrometry data. The successful candidate will work on two funded projects to identify
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with USGS scientists on field data collections. 3) Investigate the spatial patterns of fish and invertebrates in the Great Lakes. 4) Present results at scientific meetings and to regional management