24 gis-python "NTNU Norwegian University of Science and Technology" PhD positions in Sweden
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topographic indices as environmental variables for mapping, and satellite data for weather. The doctoral student will be part of a broad research group with expertise in GIS, AI, soil science, forest ecology
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Information Systems (GIS), programming in Python, practical forestry, nature conservation, cultural heritage management, as well as a driver’s license, are considered assets. Place of work: Umeå Forms
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desired. Knowledge on statistical methods and their application is an extra merit. Good knowledge in GIS and R is a merit. Proven excellence in written and spoken English is essential. The fieldwork will
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ecology, and/or restoration ecology. Experience in design, execution and analysis of acoustic data is desired. Knowledge on statistical methods and their application is an extra merit. Good knowledge in GIS
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are looking for candidates who have: Strong programming skills, particularly in Python Solid analytical and mathematical abilities Experience with machine learning Strong communication skills and proficiency in
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for candidates who have: Strong programming skills, particularly in Python Solid analytical and mathematical abilities Experience with machine learning Strong communication skills and proficiency in English The
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in fluid dynamics, turbulence modeling, CFD, and turbomachinery. Experience with CAD and CFD tools (e.g., Ansys Fluent, CFX, StarCCM+, OpenFOAM). Programming skills (e.g., Python, MATLAB). Knowledge
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or Python) - In-depth understanding of computer systems (assembly code, compilers) Important personal qualities are the ability to work in a team, communicate with colleagues, be curious and creative. About
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forests are merits. Skills in planning, executing, and reporting scientific studies, as well as experience with statistical analyses, GIS and literature reviews (incl. meta-analyses) are meriting
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-level data on single cell level, R/Bioconductor and Python/Tensorflow will be useful but is not a requirement. An experimental researcher with an interest in methodologically driven cancer research