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The Leibniz-Centre General Linguistics (ZAS) invites applications for TWO PhD research positions in general linguistics. The positions require an M.A. degree (or equivalent) and can be awarded
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PhD Neural Control of Vocal Pitch: Exploring the Intersection between Speech and Language Processing
Period of funding: 1.0 FTE, 4 years 0.8 FTE, 5 years The overarching goal of this project is to characterize the influence of linguistic context, tonal language experience, and pitch sensitivity
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PhD Neural Control of Vocal Pitch: Exploring the Intersection between Speech and Language Processing
PhD Neural Control of Vocal Pitch: Exploring the Intersection between Speech and Language Processing (V24.0349) « Back to the overview Job description Period of funding: 1.0 FTE, 4 years 0.8 FTE, 5
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a case study, your research will address methodological and theoretical aims such as studying language variation and change, developing AI tools, and managing diverse stakeholder interests
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a case study, your research will address methodological and theoretical aims such as studying language variation and change, developing AI tools, and managing diverse stakeholder interests
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language variation and change, developing AI tools, and managing diverse stakeholder interests. This project will also explore the application of FAIR principles and open data, among other topics
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impairments and language barriers. This innovation aims to maintain the cost advantage of digital survey technologies while significantly enhancing real-time, personalised supports through human-like
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to carry out international academic activities. Ability to understand and communicate in the language(s) relevant to the proposed doctoral research project. The working language of the project is
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PhD Position: Shipping Decarbonization and Trade Law at the Faculty of Law, University of Copenhagen
PhD thesis. Applicants must have excellent language skills in English and have excellent communications skills. Applicants must be able to teach at an academic level in Danish or English and to follow
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the effective and efficient design of safety-critical cyber-physical systems based on Machine Learning and Deep Learning, including deployment and applications of Large Language, Vision and Audio Models in