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- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
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conferences and contributing to collaboration between NIBIO, NMBU, and national and international research partners. Professional qualifications (required) A Master’s degree in machine learning, artificial
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methods, and structured unsupervised learning. Research Environment & Collaboration The successful candidate will work at the interface of probabilistic machine learning, computational statistics, and
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to teach robots to understand forest well enough to navigate and move through them in real time, using machine learning on LiDAR point clouds and camera imagery for real-time understanding of the forest
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relevant fields, at the time of applying Organise dissemination and communication activities within the AI LEARN centre Be able to work independently and in a structured manner, and have good collaboration
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unsupervised learning. Research Environment & Collaboration The successful candidate will work at the interface of probabilistic machine learning, computational statistics, and biostatistics, developing new
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computational image analysis, computer vision and machine learning. The aim is to develop robust and standardized methods to link structural, mechanical and biological properties to biomaterial performance and
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focus on combining simulations using spatial-genetic-demographic individual based models (e.g., using the software SLiM), machine learning approaches, and genomic data to estimate larval dispersal
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, with attention to scientific quality, reproducibility, and continuous learning. Communicate, present, and collaborate effectively, including explaining technical ideas clearly and presenting research
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learning. The aim is to develop robust and standardized methods to link structural, mechanical and biological properties to biomaterial performance and tissue reconstruction. The PhD candidate will work with
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, and a learning-oriented mindset work independently, take initiative, and maintain good structure and discipline in their work communicate effectively and collaborate well with supervisors and peers show