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design of hybrid electrical–optical computing architectures and (2) develop learning rules that are both technology-feasible and well-suited for machine-learning workloads. The project will consist among
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, and physics-informed machine learning open new possibilities for establishing grid foundation models. However, it is still unclear which model structures is most capable and suitable, how well they
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areas: artificial intelligence and machine learning; statistical learning or applied mathematics; multimodal and longitudinal data analysis; computational biology, bioinformatics or digital health
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Your Job In this position, you will join our Simulation and Data Lab for AI and Machine Learning for Remote Sensing . The lab advances interdisciplinary research and operational services by combining
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advances the progress of research. Your tasks involve: Developing a machine learning-based model to map satellite retrievals to ground based air pollutant concentrations Conducting error assessment on the
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processing conditions Apply data-driven and machine-learning methods for multi-objective optimization of efficiency, reproducibility, and operational stability Study intrinsic material stability
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opportunity to work in a world-renowned research environment. Our teams consist of passionate and dedicated experts, providing an environment conducive to learning and collaboration. You will have
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machine learning–based analysis. Behavioral features are compared at inter- and intra-disease levels to identify disease-specific signatures and establish a foundation for future circuit-level
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programming and applied mathematics, and interested in neural networks and their hardware implementation. Some experience in machine learning would be an asset. Prior knowledge of emerging memory
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Excellent knowledge and experience in programming Python Excellent knowledge and experience in machine learning Excellent ability for cooperative collaboration Very good command of written and spoken