634 machine-learning "https:" "https:" "https:" "https:" "https:" "The University of Edinburgh" positions in Norway
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exemptions from the requirements: https://www.mn.uio.no/english/research/phd/regulations/regulations.html#toc8 Grade requirements: The norm is as follows: The average grade point for courses included in
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the research fields of cardiovascular biomarker research and cardio-oncology. You can find more information about the research group here: https://www.med.uio.no/klinmed/forskning/grupper/kardiovaskular-crg
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leader will be Professor Jonathan Robert Whitlock (read more about the group at: https://www.whitlocklab.org/). About the project The successful candidate will perform large-scale neural recordings in
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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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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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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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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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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