632 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" positions in Norway
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mediators using in vivo mouse models. The position has a duration of two years. The project group is part of a vibrant and inclusive research environment (https://www.ous-research.no/kt/) at the Department
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information can be found here: https://www.ntnu.edu/mai Duties of the position Complete the doctoral education until obtaining a doctorate Carry out research of good quality within the framework described above
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inventories and provision of environmental information. Similarly, the developments in AI and machine learning allow for new and improved processing of remotely sensed data supporting precision forestry
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is internationally recognized, with interests spanning a broad range of areas - including statistical machine learning, high-dimensional data and big data, computationally intensive inference
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machine learning and AI techniques to improve prediction of contaminant transport, sediment dynamics, and ecosystem exposure in complex fjord environments. The research will benefit from extensive
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Digital Twin for façade condition, fire safety risk classification, and maintenance planning Apply statistical and machine-learning methods to link climatic loads to degradation indicators Validate models
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position as Postdoctoral Fellow in law at the UiS School of Business and Law, Department of Accounting and Law. The Postdoctoral Fellow will be affiliated with the research project AUTO-MARE – Autonomous
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modelling tools. This PhD position aims to achieve to develop by the use of automatic picking, rather than manual, travel time picks, and the application of machine learning methods to reliably pick relevant
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Digital. The research focuses on advanced signal analysis and machine learning methods that enable robust operation and service continuity in future wireless networks under challenging radio conditions. As
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optimization (WOB, RPM, flow rate, etc.) using machine learning techniques Anomaly detection for downhole vibrations, bit failure, and circulation losses Integrating physical modeling, digital twins, and data