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Computer Science, Chemistry, Chemical Engineering, Physics, or Materials Science. You will develop optimisation and machine-learning algorithms for human- and literature-informed discovery of new materials
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biomedical data science, developing new methodology and scalable algorithms, and collaborating with interdisciplinary teams at Duke. Duke is an Equal Opportunity Employer committed to providing employment
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length scales Develop machine learning algorithms to support process optimization, predictive modeling, and intelligent manufacturing control Integrate simulation tools with in-situ sensor data from
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members. Participate in traffic scenario generation project and pedestrian modeling project. Develop sophisticated AI-driven algorithms that create realistic, safety-critical test scenarios for autonomous
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evaluate advanced algorithms for applications such as secure and adaptive control, anomaly and attack detection, resilient decision-making, and AI-enabled operational support for highly distributed grids
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estimation. Designing predictive control strategies that regulate muscle-tendon loading via wearable exoskeletons. Implementing and testing control algorithms in simulation and real-time settings. Where
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research. The ideal fellow will be interested in developing and applying novel computational algorithms to novel datasets generated in the setting of non-neoplastic and neoplastic disease. Key
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under the supervision of Professor Rachel Humphris. They will lead in-depth ethnographic fieldwork in the Netherlands, undertaking interviews and participant observation to trace how welfare algorithms
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out from a longitudinal perspective linked to the following three media regimes: television regime (1980s-1990s), digital regime (2000s-2010s), and algorithmic regime (2020s). It will be compiled using
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-terrestrial networks, and the performance evaluation of these solutions in challenging IoT scenarios. The position involves both theoretical and experimental research, including algorithm design, simulation