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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
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, machine learning, and control in the energy sector. The postdoc researcher will perform theoretical study and algorithm development on optimization/control/data analytics methods and authorize peer-reviewed
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project focused on the development machine-learning powered digital twin system for the structural performance of civil engineering structures. The project is a collaboration between multiple research
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decays, searches for supersymmetry and other new phenomena, and measurements of rare standard model processes. We vigorously pursue the use of machine learning techniques for data analysis. Candidates must
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Standing or sitting up to 8 hours/day, lifting <30 lbs, may view computer up to 8 hours/day. Shift Some flexibility, generally Monday- Friday, 8-5 Job Summary This position is designed to have 60-70 percent
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and analysis of mathematical methods for novel imaging techniques and foundations of machine learning. Within the project COMFORT (funded by BMFTR) we aim to develop new algorithms for the training
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at one of OCP Group’s production sites. The project will rely on Operations Research and Machine Learning approaches. The objective is to redesign the extraction methods by considering their impact on
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the ability to work together with colleagues and teach and mentor students from diverse backgrounds and perspectives. To apply, candidates should submit a cover letter, curriculum vitae, and contact information
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vision, IoT sensors, and blockchain to monitor food quality, safety and animal welfare in real-time and enhance transparency. AI and machine learning will analyse data from pilot sites to identify
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physics. Development of new artificial-intelligence and machine-learning techniques for high-energy and nuclear physics. Close interaction with our collaborators in the EIC and the BNL theory group will be