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well as resource limitations. The core research objective of this PhD is to design and evaluate “latency hiding” methods for immersive networked interactions. This involves (i) developing predictive machine learning
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an interest in investigating the effects of peatland restoration on hydrology and catchment biogeochemistry by means of a diverse set of methods, including field investigations, modelling and data-driven
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and limitations arising from the use of AI-based methods in predictive feedback. The successful candidate will: explore how a combination of multimodal observation (audio, video, LIDAR, thermal vision
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of limited temporal and spatial accuracy of such remote interactions. We pay particular attention to the exploration of potential and limitations arising from the use of AI-based methods in predictive feedback
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the workplace. The second supervisor conducts research on diversity and inclusion and has expertise in research methods. The third supervisor conducts research on organizational psychology and innovation. Project
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, electrical engineering, human-computer interaction, or other relevant fields. The applicant is required to document that the degree corresponds to the profile of the post. Grade average of B or better
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collaborators. The position is part of the ERC Starting Grant “Actively learning experimental designs in terrestrial climate science (ACTIVATE)”: https://www.mn.uio.no/geo/english/research/projects/activate
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credentials. Required qualifications: Applicants must hold a Master's degree or equivalent in informatics, computer science, biomedical engineering, electrical engineering, human-computer interaction, or other