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nanoparticles. The successful candidate will also learn cutting edge deep-sequencing approaches to evaluate off-target editing within the genome. They will have the opportunity to participate in meetings
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closely with our collaborators to establish a deep learning-based image analysis pipeline. The successful applicant should hold a PhD in cell biology or neuroscience. Previous experience in live cell
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Machine Learning, and has over 30 PhD students, postdoctoral researchers and faculty members working on a broad variety of deep learning, computer vision, and foundation model subjects, like self-supervised
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Do you have a background in deep learning and computer vision? Are you independent, creative and eager to take initiatives? Do you enjoy working in an international research group and interacting
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each other. This necessitates a multidisciplinary approach bringing together optimization, machine learning and behavioral modeling methodologies. In FlexMobility we propose a holistic approach to design
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, and geopolymer encapsulation). Two high-level waste approaches (e.g., deep borehole disposal vs. deep geological disposal) will be analyzed. Case study data for the two BWRX-300 units will be
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the attractiveness to the users, we need innovative designs where fixed and flexible services support each other. This necessitates a multidisciplinary approach bringing together optimization, machine learning and
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apply a fast and efficient forest trait mapping and monitoring method based on the Invertible Forest Reflectance Model. A machine learning / deep learning framework will be explored and developed
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to provide hands-on support in the lab to research being carried out by MSc students, PhD- and postdoctoral researchers in the QG&QI, QMat and SM research clusters. You will play an important role in the QG&QI
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, PhD- and postdoctoral researchers in the QG&QI, QMat and SM research clusters. You will play an important role in the QG&QI research cluster, for example building new laser systems, helping new students