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in each subject are specified in the relevant general syllabus, available at https://www.lusem.lu.se/research/doctoral-studies. Other requirements A proficient level of English is required in both
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Research Assistant/Assistant Professor (Postdoctoral Researcher) for the project 2025/57/B/NZ8/03444
(ALS) data and multispectral imagery, methods for spatial data analysis and the use of GIS tools and geospatial workflows, working with environmental datasets, in particular those related to forest
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intelligence, large language models (LLMs), geospatial analytics, and behavioural science to generate actionable insights for rural communities, insurers, and stakeholders. The postholder will design and
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Archive, U.S. Forest Inventory and Analysis program, sPlotOpen) along with geospatial soil and climate data to apply resource colimitation theory in the refinement of correlative-based species distribution
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, Engineering, Geospatial Science or a closely related field is required. PH.D. is preferred. The successful candidate must have significant coursework and/or expertise in land surveying. Preference will also be
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, including time series analysis and statistics (e.g. mixed effects modelling) Capacity to develop computer code and experience with programming languages (Matlab, Python, R) and geospatial tools (e.g. ArcGIS
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selection and advancement decisions within breeding programs. Qualifications Required Qualifications M.S. degree in Plant Breeding/Genetics, Plant Science, Agronomy, Agricultural Engineering, Geospatial
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requirements for doctoral studies in each subject are specified in the relevant general syllabus, available at https://www.lusem.lu.se/research/doctoral-studies. Other requirements A proficient level of English
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Geospatial analysis, machine learning, and predictive modelling, Have a good command of programming tools such as R packages, Phyton, and other programming languages Publications in the field Excellent
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decision support Analysis of large-scale geospatial, sensor, and remote sensing datasets (e.g., multispectral, hyperspectral, LiDAR) Predictive modeling for crop performance, resource efficiency, and climate