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, investigate error mitigation techniques Cooperate and actively work with international collaborators Your Profile: Master’s degree in physics, mathematics, or computer science Strong interest in both developing
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. This exciting project will focus on designing, constructing, and testing synthetic cells with multiple sub-compartments. Just like their living counterparts (i.e. eukaryotic cells), synthetic cells with different
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addition to debits, credits can be obtained. The research performed under this internship by an Electrical/Power Engineering or Computer Science PhD student will relate to this reform, specifically investigation
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and rural health staff informatics, coordinate the writing of ethical applications and reports, and contribute to writing publications and external grants. To be successful you will need: A PhD in
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”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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relevant to multiple applications, including small aircraft, drones, turbines, and other systems reliant on efficient fluid flow around foils. The project offers a unique opportunity to gain experience in
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institutions around the world Continuous scientific mentoring by your scientific advisor as well as feedback and wide-ranging expertise from the whole group in multiple facets of quantum technology and
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Science or German Diplom, preferably in biology, chemistry/biochemistry, psychology, medicine, physics, engineering, or informatics neuroscientific knowledge and interest in an interdisciplinary research
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(pre-screened) by multiple people independently. Personal interviews follow in the next step. Language requirements Proficient English skills Submit application to E-mail: application@mcml.ai
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. Development of multimodal AI models that fuse data from multiple types of sensors to accurately model and predict wind turbine blade damage. Establish and develop data science pipelines for wind turbine blade