81 gis-python-"NTNU---Norwegian-University-of-Science-and-Technology" positions in Sweden
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, 55% during 2026 and estimated between 20–40% from 2027), with a focus on providing expert support and developing solutions for research projects that require expertise primarily in GIS and network
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on individual assessment. Special weight will be given to: Documented experience in analysing ecological or biological data using Python or R. Basic knowledge of database management (e.g., SQL), data retrieval
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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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pipelines in Python and R. Perform experiments in cell culture and animal models to validate the findings. Coordinate the collaboration between the chronobiology lab (led by Dr. Paul Petrus) and the
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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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Machine Learning Integration Develop and implement machine learning algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC
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in mouse models and cell cultures. Analyze and interpret omics data using bioinformatic pipelines in Python and R. Perform experiments in cell culture and animal models to validate the findings
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algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC) to accelerate design iterations Integrate ML approaches with finite