14 computational-"Multiple"-"NTNU---Norwegian-University-of-Science-and-Technology" PhD positions in Denmark
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an international, interdisciplinary research environment. At DTU, you will be part of the PCAS, and you will find yourself among multiple PhD students and senior researchers working on multiple aspects
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Computing at DTU Compute, and the Danish Glaucoma Association. The project, called "Project FOREVER: Application of deep learning models for better glaucoma detection in practice” is a subset of the ongoing
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Job Description Do you have a background in bioinformatics or AI/ML? Do you wish to do a PhD whereby you use your computational skills to discover new insights in industrially important bacteria
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consumers. You'll gain deep interdisciplinary experience—combining multiple data layers and approaches including bioinformatics, machine learning, food safety management, regulatory science, genomics and user
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undermine this future. Can you see how Machine Learning, Computer Vision, and Robotics can open up opportunities for autonomously operating agricultural robots? Are you passionate about making agriculture
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funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The Climate and Energy Policy
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graph using RDF, OWL, and related technologies Designing and implementing workflows for data ingestion, integration, and querying across multiple systems Driving use-case studies that demonstrate
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optimization. Experience with energy system modeling - ideally of large scale multiple country energy systems, PtX and renewable fuel production. Strong writing and presentation skills. A willingness and desire
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qualifications As our new colleague in our research team your job will be to develop novel computational frameworks for machine learning. In particular, you will push the boundaries of Scalability, drawing upon
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development and marine management. Your primary tasks will be to: Compile and harmonize data from multiple sources (e.g., EMODnet, Copernicus, fisheries surveys, citizen science). Engage with data managers and