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Apply now Job no:501140 Work type:Full time Location:Hobart Categories:Research Focused Lead wildlife-tech research to protect forests using drones, AI, and spatial ecology A dynamic role open to
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statistical / data science languages such as R or Python Experience working with geographic information systems Desirable Characteristics: Experience with building spatially explicit models, including
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record of publications in top-tier venues such as SIGMOD, VLDB, ICML, NeurIPS, ICLR, or TPAMI. You may also: Have a strong background in machine learning, particularly foundation models for spatial data
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benefit both Indigenous communities and the environment. The fellow will apply their expertise in spatial analysis, remote sensing and geospatial modelling to support healthy Land and Sea Country management
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the context of spatial-temporal data analysis (ideally with geoscience applications). Proven ability to design, train and implement joint modality deep learning representation learning models by integrating
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for disease prevention and control. ODeSI has extensive experience in operational research and field surveys (including international projects), predictive risk mapping and modelling (spatial epidemiology