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Field
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structured and unstructured biomedical data, including EHR data, wearable sensor time-series data, or clinical annotations. Demonstrated ability to build interactive or web-based systems using frameworks with
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sensor data, public databases, and GIS. Predictive Modeling:Design predictive models to evaluate the impact of urban and environmental policies on public health. Interdisciplinary Collaboration:Collaborate
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with IoT technologies (smart sensors, connected devices) for real-time monitoring of waste systems. Circular Economy: Familiarity with circular economy principles as applied to waste management
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EEG HydroCel Sensor system and Net Station software and interest in the microstructure of infant sleep would be a plus. The successful candidate is expected to lead their own project, support other
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Max Planck Institute for the Structure and Dynamics of Matter, Hamburg | Hamburg, Hamburg | Germany | about 1 month ago
Experience in HPC computation (application and algorithm/code development) Willingness to closely collaborate with experimentalists and theoretician. Joint research approach of all ERC synergy team members
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physical and chemical characterisation. There will also be opportunities to integrate these materials into functional devices, such as sensors, and evaluate their electrical, optical, and thermal performance
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We are seeking motivated researchers with expertise in large-area 2D materials growth for sensor and transistor applications. The focus is on developing wafer-scale 2D materials using CVD, PLD
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relationships; medical and community partnerships; and real time ecological momentary assessment of health via data from sensors, accelerometers, and smartphone technology. The successful candidate will join a
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algorithm sensing, navigation and control of robot systems, including mobile robots and robot arms/manipulators; designing and implementing algorithms for machine learning, computer vision, and/or estimation
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the developmental rules underlying phenotypic variation. The successful postdoctoral fellow will develop and implement an empirical framework that utilizes data-driven algorithms to learn relationships between past