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Field
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of civil protection assets; - Researching and developing possible ways of calculating the risk of exposure of civil protection assets; - Applying machine learning algorithms to assist in risk calculation
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advantages. We will provide the necessary hardware and software for the real-time control of the machine, but the candidate will be responsible for developing and implementing the control algorithms. A working
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the project is the development of AI-based pipelines for detecting, segmenting, and classifying lichen communities. Convolutional neural networks (e.g., U-Net, DeepLab) and machine-learning algorithms (e.g
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science, business development and Information Systems. The Norwegian government’s ambitions to reduce climate gas emissions by 55% by 2030 can only be achieved through the massive electrification of sectors
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currently deposited at Zenodo. We will re-annotate the proteins to comprehend the underpinnings of fungal ecology and adaptability. The project will integrate information on the evolution of Mortierellomycota
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Computer Science• Distributed systems (Cloud, Edge)• Scheduling and optimization algorithms• Data streams, data management and data pipelines• software engineeringExpert programmer• Java• at least one other
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candidate will contribute to: Developing supervised deep learning algorithms for 3D point clouds Developing self-supervised deep learning algorithms for 3Dpoint clouds Expand for a wider variety of downstream
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contribute to: Developing supervised deep learning algorithms for 3D point clouds Developing self-supervised deep learning algorithms for 3Dpoint clouds Expand for a wider variety of downstream tasks focused
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preprocessing IoMT network traffic datasets. Implement and evaluate machine learning algorithms (e.g., logistic regression, SVM, random forest) for intrusion detection. Develop prototype software tools (e.g
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Are you eager to advance lung cancer diagnostics by developing standardized biomarker assays using state-of-the-art mass spectrometry? Join us as a PhD candidate and contribute to clinically