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synchrotron X-Ray methods. The Postdoctoral Associate will develop new methods for measurements, data analysis and modeling for Modulation Excitation Spectroscopy and will travel to US and foreign synchrotrons
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://pritykinlab.princeton.edu) develops computational methods for design and analysis of high-throughput functional genomic assays and perturbations, with a focus on multi-modal single-cell, spatial and genome editing
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of reliable and robust deep learning methods with clinical impact. The project is highly interdisciplinary, and the successful candidate is expected to interact with clinical and industrial partners
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and analysis methods. Preferred Field of Expertise: Publications in peer-reviewed journals in the same or related field. Skills: Other: Analysis Assessment/evaluation Communication -- written and oral
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methods such as functional magnetic resonance imaging (fMRI) and psychological experiments. Keywords: fMRI, hyperscanning fMRI, Social brain * details of the business RIKEN is Japan's largest and most
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frameworks. Experimentally validating these methods through field experiments with real-world robotic platforms. The Postdoctoral Scholar will also collaborate closely with the laboratory members to execute
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of biomolecules, it is crucial to grasp their structural dynamics. We aim to develop methods for designing biomolecules, such as membrane proteins and peptides, with controllable conformational dynamics through a
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synchrotron X-Ray methods. The Postdoctoral Associate will develop new methods for measurements, data analysis and modeling for Modulation Excitation Spectroscopy and will travel to US and foreign synchrotrons
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computing, data science, or human-computer interaction. Programming skills needed to do data analysis (we mainly use Python) You can be interested either in theoretical methods development or applications
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be the state estimation of the robotic system from external cameras. Familiarity with existing methods from these domains, such as Deep Learning, Quality-Diversity algorithms, reinforcement learning