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- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
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the Marie Skłodowska-Curie Doctoral Network (ENDOTRAIN). Join Europe’s first doctoral network in digital endocrinology – integrating AI, sensor technology, omics, and clinical medicine to transform diagnosis
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system integrates robotics, automated sample handling, sensor networks, imaging systems, cloud computing, and machine-learning-based analytics. The research work at NTNU will focus particularly
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monitoring 96 parallel cell-culture experiments under precisely controlled environmental conditions. The system integrates robotics, automated sample handling, sensor networks, imaging systems, cloud computing
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to the properties of live sensor data: sparse, unevenly sampled, motion-distorted and partially observed Implement and evaluate the resulting systems on embedded hardware and on NIBIO's robot platforms Professional
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lipid fermentations by integrating data from online spectroscopy, standard bioreactor sensors, and lab-scale bioreactor experiments across various oleaginous microorganisms. The candidate will build and
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doctoral degree Collect, structure and assess relevant sensor, operational, maintenance, incident and cost data Develop and validate statistical, causal and/or machine-learning methods and turn the results
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the Norwegian Railway Directorate. Duties of the position Complete the doctoral education, including at least 30 ECTS of coursework, and obtain a doctoral degree Collect, structure and assess relevant sensor
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working with heterogeneous datasets originating from different sensors or data sources. Familiarity with forest ecology, biodiversity, or natural-resource applications. Experience developing reproducible
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. Thus a background in fluid mechanics (marine hydrodynamics or aerodynamics) is required. Knowledge of measurement technology and relevant sensors, as well as industry experience from the industry
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with researchers and practitioners through the aiD network and related national initiatives. Contribute to the scientific development and collaborative activities of the Data Science research group