17 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" positions at Lulea University of Technology
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and Artificial Intelligence (RAI) ( https://www.fieldrobotics.eu/ ) based at Department of Computer Science, Electrical and Space Engineering as part of the national research program WASP. Wallenberg
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participating in the DARPA SUB-T challenge with the CoSTAR Team lead by NASA/JPL ( https://costar.jpl.nasa.gov/ ). Subject description Robotics and artificial intelligence aim to develop novel robotic
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work within your research studies as well as communicate your results at national and international conferences and in scientific journals. The tasks will include laboratory tests, analysis of data, and
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international conferences and in scientific journals. The tasks will include laboratory tests, analysis of data, and modeling. Qualifications To be eligible, you must have a master’s degree in materials
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Postdoc position in Mining and Rock Engineering - “Advanced data mining of computerized mining equipment in underground mines” Lulea Reference number 3320-2026 Do you want to develop your research
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approach. Your tasks are to: - reprocess and jointly model the available regional magnetotelluric (MT), gravity and magnetic data to develop an initial model for screening the subsurface to select areas
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importance due to the need for high-quality data and information to monitor, make decisions and take correct actions in almost every process that involves automation. The Postdoctoral researcher will
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PhD Student in Urban Water Engineering Lulea Reference number 3514-2026 Data-driven understanding & predictive modelling of sediment accumulation in urban drainage infrastructures Are you ready to
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Postdoctor position in Urban Water Engineering Lulea Reference number 3516-2026 Data-driven understanding & predictive modelling of sediment accumulation in urban drainage infrastructures Do you want
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harvesting and characterization; trace-element analysis; geochemical or mineralogical methods; and statistical analysis of multivariate experimental data. Personal Competencies Strong analytical and problem