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open-source materials, remote sensing and geospatial data, and survivor and witness testimony. The Centre's methodological approach is interdisciplinary, and the successful candidate will benefit from
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access to extensive national forest datasets and geospatial information, work within an interdisciplinary research environment, and contribute to developing the next generation of AI methods for large
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. Experience working with geospatial data and spatial analysis, or the ability to learn. Experience analysing whole genome libraries, including quality control, sequence alignment, and calculating population
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, experience working with large-scale geospatial datasets and high-performance or cloud computing, and demonstrated expertise in advanced machine learning. Experience leading research projects, managing budgets
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formulate research questions at the intersection of AI, EO and advanced computing Develop and investigate AI methods for multisource EO data, exploring approaches such as geospatial foundation models and
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data (e.g. instrumental, laboratory, environmental, numerical and categorical data, text, geospatial data or time series); c) Experience in Python programming and in data analysis and machine learning
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for admission. The project would suit a candidate with a background in surveying/geodesy, geomatics, remote sensing, geospatial science, civil/environmental engineering, hydrology, Earth observation
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must have earned a Ph.D. in wildlife science, landscape ecology, geospatial science, waterfowl ecology, or a closely related field. Preferred qualifications include experience with: GPS telemetry and
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component: - Undertake advanced training in qualitative, quantitative, participatory and geospatial research methods. - Participate in seminars, scientific meetings and training activities organised by
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, geospatial analysis, future-crafting approaches, and novel simulations known as ‘peace- gaming’. Students will also have opportunities to collaborate with our institutions’ partners around the world, across