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PhD scholarship in Runtime Multimodal Multiplayer Virtual Learning Environment (VLE) - DTU Construct
behavior and provides active personalized learning for improved instant decision making. Key beneficiaries are expected to be construction industry stakeholders, for example, project owners, architects
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subgroups—in shaping how teams collaborate and perform under pressure. By advancing knowledge on these dynamics, we seek to contribute to both scientific theory and practical solutions for organizations. Your
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public datasets as well as samples generated through collaborations. Ideal candidates will have strong interests in microbiome-host biology, bioinformatics, or machine learning. Experience with R or Python
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algorithms for optimization Quantum annealing Quantum inspired optimization Quantum machine learning with a special emphasis on classical optimization of QML algorithms Noise mitigation in relation
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position to work at the intersection of transportation science, operations research, and transport economics. The PhD position is part of a strategic collaboration between the Technical University of Denmark
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society from various theoretical perspectives and develop new responsible digital technology. Our other priority area of research is Language in Learning, which aims to enhance collaboration across language
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predicting and improving adherence through Artificial Intelligence (AI) and machine learning (ML). Your colleagues: An interdisciplinary team of scientists working across Maastricht University and FH Joanneum
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support departments. Our academics collaborate in six research centres and teach more than 1,000 students. Our staff and students are committed. The lines of communication are short and each individual
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Section, Department of Civil and Mechanical Engineering, Technical University of Denmark (DTU). We look for a talented, self-driven, and collaborative individual with a passion for tackling complex
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learning, offers powerful solutions to automate these tasks and provide reliable real-time information. This doctoral project is part of a 5-year research chair on Computer vision applied to the swine sector