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Field
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reasonable duties requested commensurate with this role. Your qualities Essential: A PhD in Social Anthropology, Human Geography, Environmental History, Environmental Politics or other connected fields
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for 1.5 years, and will be embedded within the UMC Utrecht AI-Lab initiative. Your primary focus will be the development of AI models for defect detection and process optimization of tissue constructs
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representation of real-world marine structures and their corrosion processes. By integrating comprehensive sensor measurements, experimental data on corrosion, and advanced predictive modelling (including physics
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or uncertainty propagation through the pipeline process; investigate which EO data can be most suitable for different climate adaptation/mitigation situations (e.g. required spatial and temporal resolutions
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Education You should have recently completed, or be close to completion of a PhD in a related technical or scientific discipline. Preference will be given to applications submitted by candidates within
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of machine learning (ML) and quantum many-body physics. We are also happy to work with experts in one of the two fields who are committed to learning the other. Moreover, we look for interest in developing
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, open to communicate and collaborate with peers and partners. You also possess: A PhD in Agronomy, Environmental Engineering, or related areas Experience with agricultural practices, agrobusiness, energy
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for this position, the following is required: PhD in a relevant field such as computer science, quantum physics, electronic engineering, data science, AI, machine learning, Earth system science, climate etc. with a
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Applications are invited for a Postdoc position within the research project “Uncovering the creative process: from Inception to Reception of translated content using machine translation” (INCREC
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retrieval pipelines and instead integrates the indexing and retrieval process into a single, end-to-end generative model. As such, GenIR models are able to more deeply interact with the underlying corpus