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information about our institute here: https://www.fz-juelich.de/en/ias/ias-8 Your Job: Develop physics-aware simulations of growing cell populations, including their spatiotemporal manipulation in microfluidic
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developing prototype systems and algorithms • Experience with MRI and/or fMRI data (human data preferred) • Experience with high resolution and high field MRI and/or fMRI • Excellent verbal and written
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development of wide bandgap device-based integrated motor drives, advanced control techniques for next-generation drives, and innovative approaches to reducing passive components in electric drive systems. Your
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for the Advancement of Surgery) initiative. The Research Associate will be responsible for developing machine learning algorithms and creating predictive models. The ideal candidate must demonstrate a robust background
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in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment focuses on methodological development in cryo
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scientists, biomedical informaticians, clinicians, and public health researchers to develop deployable, trustworthy methods that improve patient outcomes and health system operations. Key responsibilities
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Doctoral Candidates (DC1 and DC2) to carry out research in neuromorphic photonic-electronic integrated circuits for brain-inspired information processing and sensing (DC1) and in the development of efficient
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transmission of information and energy, systems theory, and computational hardware and software. ECE students are encouraged to develop synergies with disciplines outside of engineering. The candidate should
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of this PhD project is to develop machine learning algorithms that perform efficiently and coherently across both classical and quantum computing platforms. The PhD project falls under the collaboration between
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schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . To develop novel inertial microfluidics and impedance-based cell sorter. To develop novel label-free CQAs based