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experience in the following areas: Knowledge of computer science and machine learning. Familiarity with electrical and electronic engineering. Proficiency in programming languages such as Python, C++
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19 Mar 2026 Job Information Organisation/Company Aarhus University Research Field Engineering Other Researcher Profile First Stage Researcher (R1) Recognised Researcher (R2) Positions PhD Positions
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the project RIBOTICS (RNA Origami Technology in Cell Systems) funded by an Advanced Grant from the European Research Council (ERC). The intended starting date is 01st September 2026 or as soon as possible
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the project RIBOTICS (RNA Origami Technology in Cell Systems) funded by an Advanced Grant from the European Research Council (ERC). The intended starting date is 01st September 2026 or as soon as possible
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Postdoctoral position in the development of an AI-based phenotyping system for high-throughput sc...
work. Qualifications PhD in computer science, computational biology, engineering, or related fields. Experience developing deep-learning tools for image processing, automatic monitoring of agricultural
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work. Qualifications PhD in computer science, computational biology, engineering, or related fields. Experience developing deep-learning tools for image processing, automatic monitoring of agricultural
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Postdoctoral Researcher in Natural Language Processing and Digital Humanities (18 months, full-time)
Intelligence, Machine Learning, or Computational Linguistics Digital Humanities or Linguistics with a strong computational focus Classics, History, Philology, or related humanities disciplines with documented
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inverters to enhance grid flexibility, reliability and stability. • Apply machine learning and AI tools for the battery system health estimation and maintenance prediction and integrate analytics
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writing and presentations at conferences and workshops. Qualifications: PhD in Biomedical Engineering, Medical Robotics, Computer Science or a closely related field Strong background in medical image
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of strains) to in-field testing of up to 800 strains. The scale and standardized approach will create a unique foundation for advanced data analysis, including AI, machine learning, and statistical modelling