82 parallel-computing-"Multiple" PhD scholarships at Technical University of Denmark in Denmark
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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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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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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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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
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oxidation and disinfection processes. Work together with researchers at multiple departments at DTU and NTNU Research activities will mainly be carried out at DTU Sustain with research visits in Norway
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collaborative settings and wish to play a key role in an EU-funded project with researchers from multiple countries? If so, this PhD position could be a good opportunity for you. This project focuses
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of diverse teams with multiple technical and theoretical expertise. Applicable responsibilities for both positions: You are expected to be able to organize and perform your own experiments, and critically
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modeling of complex information systems, and SDU and the Royal Danish Defence College’s established intelligence studies and practice. By introducing computational modeling to traditional intelligence