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capabilities. The candidate may work on advanced multimodal sensor fusion, environmental perception, field-aware localization, precision navigation, and interaction with ground-based robotic or stationary
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minimise such loss through the use of generative AI to augment training sets is proposed. Multimodal sensing: The use of open-loop or simplified (e.g., single-sensor) closed-loop systems in food automation
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three-year PhD–positions related to use of AI for mapping of forest ecosystems. New sensors and increased digitalization generate vast quantities of data that together with advanced statistical methods
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(ENDOTRAIN). Join Europe’s first doctoral network in digital endocrinology – integrating AI, sensor technology, omics, and clinical medicine to transform diagnosis and treatment of adrenal diseases. Digital
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UiA-CERN PhD Position in Multi-robot Mapping and Environmental Data Sharing - Uncertain Environments
aims to design and implement a cloud-based architecture for storing and managing maps and associated sensor data, enabling data sharing across multiple robots and missions. It will focus on compact data
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level and across entire wind parks. Modern turbines are equipped with sensors that collect large amounts of operational and environmental data, yet translating these heterogeneous data streams
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level and across entire wind parks. Modern turbines are equipped with sensors that collect large amounts of operational and environmental data, yet translating these heterogeneous data streams
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, computer science, computer vision, machine learning, state estimation, perception, sensor fusion, autonomous systems, navigation and control Deep foundation in modern machine learning Solid programming skills in C
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available in Digital Endocrinology in the Marie Skłodowska-Curie Doctoral Network (ENDOTRAIN). Join Europe’s first doctoral network in digital endocrinology – integrating AI, sensor technology, omics, and
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selection criteria Knowledge and skills in the following areas: Robotics, computer science, computer vision, machine learning, state estimation, perception, sensor fusion, autonomous systems, navigation and