216 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions in Denmark
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of Danish or willingness to learn The application must contain the following: A statement outlining your reasons for applying and your intentions and visions for the position. Your curriculum vitae, including
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observations, and remote sensing data to assess the impact of global change on ecosystem productivity and sustainability. You will develop novel algorithms to integrate data-driven machine learning and process
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methods to enhance student learning. It oversees a graduate programme in Population Studies. The centre fosters an inspiring academic environment and strives to attract and retain talented scholars
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relevant tasks. The department can offer postdocs who teach up to 20% of their working hours, the Teaching and Learning in Higher Education programme without hourly compensation. Qualification requirements
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perception will be a plus. Candidates with expertise in either modality are also encouraged to apply. Ideally, applicants for the position should satisfy the following requirements: PhD degree in vision
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. Close collaboration with our neighboring Departments (Mechanical Engineering, Electrical & Computer Engineering, Molecular Biology & Genetics, iNano, Biosciences, Food, Agroecology, and Chemistry) is a
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requirements: PhD degree in vision science, psychology, cognitive (neuro)science, or a related field Expertise in visual perception. Experience with auditory or multisensory perception is a plus. Good command of
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, a large initiative funded by the Danish Ministry of Foreign Affairs and managed by Danida Fellowship Council. Ethio-Nature aims to optimize the use of machine learning and remote sensing to site
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carriers within defects. The charge transport will be implemented stochastically to mimic nature. A significant focus of the project will be to apply machine learning techniques to optimize the model and
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Natural Language Processing, Machine Learning, or a similar area. Expertise in large language model architectures and training paradigms (transformer models, fine-tuning strategies, RLHF, etc.). Interest in