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PhD scholarship in Runtime Multimodal Multiplayer Virtual Learning Environment (VLE) - DTU Construct
-tech industries (HTI), and communities to cascading hazards, particularly NaTech (Natural Hazard Triggering Technological) events. REUNATECH is a Horizon Europe Marie Skłodowska-Curie Doctoral Network
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. It is notable that this PhD scholarship is part of the Marie Skłodowska-Curie Doctoral Network called ‘NATECH Risk management and Resilience of High-TECH industries and Critical infrastructures across
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in planning and executing computational and experimental research incl. studies on user experience, usability, and net promoter scoring Effective communication and teamwork abilities, with a commitment
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communities to cascading hazards, particularly NaTech (Natural Hazard Trig-gering Technological) events. REUNATECH is a Horizon Europe Marie Skłodowska-Curie Doctoral Network that aims to educate and train the
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dynamic community of PhD students and actively supports diversity. We are looking for a motivated applicant with good competences in operation research who wants to gain hands-on experience in cutting-edge
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working with live animals. Excellent skills for data handling and statistical analysis. Strong written and oral communication skills in English. Ability to work both independently and as a part of a team
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robustness of fish with different microbiome profiles. Responsibilities and qualifications Your focus will be to examine the mucosal microbial communities and health and welfare indicators in rainbow trout
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and dissemination activities within DTU courses and networks We are looking for a candidate who has: A Master’s degree in bioinformatics, microbiology, food science, data science, or a related field
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, including a large international collaboration, offering excellent opportunities for networking with researchers and fellow PhD students, particularly in Sweden, Norway, and Portugal. Responsibilities and
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directions will be pursued to enhance column generation using machine learning. The first line of research focuses on improving scalability by using Graph Neural Networks to identify and eliminate non