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the referee’s contact information when you submit your application. We strongly recommend that you make an agreement with the person in question before you enter the referee’s contact information, and that you
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nexus supported by data-driven methods. This is a full-time position, expected to start on 1st of February 2026 or as soon as possible thereafter. This position is a six-year position as an assistant
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conditions for art and culture. Possible focal areas include AI and algorithmic creativity, digital media aesthetics, data-driven culture, new forms of the dissemination of art, literature, theatre and music
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, such as motion capture, electromyography, inertial measurement units, advanced data processing, numerical methods, optimization procedures, statistics, research ethics and management of personal data
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. More specifically, the PhD position will look towards connecting different advanced software tools (of multi-physics and data-based models) simulating the metal AM process & microstructure with
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are in part funded by a DFF: Sapere Aude project (“Building TRUST in Text: Linguistically Motivated Language Model Detection”) and an NNF: Ascending Data Science Investigator project (“LM2-SEC
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handle multiple priorities at once, solve problems independently, and adapt quickly to changes. You understand the importance of confidentiality and trustworthiness and handle sensitive information with
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professional information from Associate Professor Kristian Olesen, Research Group of Planning for Urban Sustainability, 99407211, kristian@plan.aau.dk. Qualification requirements Appointment as assistant
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Postdoctoral Position in the Research Unit for Robophilosophy and Integrative Social Robotics (RISR)
culturally sustainable social robotics as developed at the research unit RISR. (Background information on ISR is provided at the project’s webpage). DARE will also benefit from close collaborations with
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to: Gas sensor selection and characterization Embedded system integration Mechanical design (CAD) and 3D printing Data collection and analysis Basic machine learning for sensor data interpretation Start