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and collaborative environments. Required Education: PhD from an accredited college or university in Educational Technology, Learning Technologies, Human-Computer Interaction, or a related field
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The University of Vienna is a cosmopolitan hub for more than 10,000 employees, of whom around 7,500 work in research and teaching. They want to do research and teach at a place that suits their
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 18 hours ago
://drive.google.com/file/d/17SY2HfckkPZp8GVtRelkIxrGhikP92nN/view?usp=sharing Job Requirements REQUIRED: PhD in materials science, physics, electrical engineering, chemical engineering, or a related field by the start
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Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) PhD degree (or foreign equivalent) in Biomedical Sciences or a closely related field. Strong competence in
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developing cutting-edge active-learning (Bayesian optimisation) methods that integrate chemical knowledge by capitalising on Large Language Models (LLMs) as well as human knowledge. You should have a PhD in
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economic modeling, with interests including improved spatial resolution and machine-learning-enabled approaches for policy analysis. Postdoctoral Position (f/m/d) – Integrated Assessment Modeling (Climate
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structures. You will work closely with computational researchers to gather data, evaluate AI predictions, and design experiments. You will work in a team with 7 PhD-students and 4 postdoctoral researchers and
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correlations. - Analyse experimental data mixing both quantum optics and solid-state physics phenomena - Supervise PhD students and interns - Ensure an efficient collaboration and information sharing with
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involved in perceptual judgments and economic decisions. Projects may involve the accuracy of judgments about probability, value, or risk; learning to forecast or categorize; the role of imprecise perception
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projects within the CUS related to urban sustainability, environmental monitoring, and urban resilience. Key Duties • Design and implement machine learning and deep learning models for hydrological