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growth. Develops innovative solutions and novel approaches to space systems challenges, including new communication architectures, radiation mitigation strategies, advanced control algorithms
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systems. Key Responsibilities Develop graph-based (multi-)omics analysis algorithms Benchmark graph-theoretic against graph-ML approaches Analysis of food-related (multi-)omics data Your Profile The ideal
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electrical stresses. Specific goals include: - Development of a hybrid model combining degradation indicators and AI-based algorithms. - Integration of the model into an online monitoring framework
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National Aeronautics and Space Administration (NASA) | New York City, New York | United States | 28 days ago
climate system and the effect of ice-atmosphere feedbacks (e.g., ice albedo feedback and cloud feedback) on sea ice evolution. Developing radiative transfer algorithms to improve both the physical
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, and shape a new direction in quantum-omics integration. Your responsibilities will include: Lead Methodological Research: Develop innovative quantum-inspired algorithms for omics data analysis and multi
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. - Contribute to the development of risk-prediction tools, biomarker panels, and precision-medicine algorithms. - Participate in NIH-funded translational studies involving spatial multi-omics, proteomics
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designing and improving general algorithms for algebraic cryptanalysis, or developing concrete attacks against proposed ciphers. This PhD position is an opportunity to explore and shape your own research
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of codes and algorithms. We will focus on devising computational solutions that can immediately be of use in other applications contexts as well. The candidate’s work will entail the development and
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transport, supply chain and logistics modelling to contribute to the development and use of the MILES (Multimodal Integrated Logistics for Simulation) and MATRA (Multi-Agent Transport Resilience and
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multiphase phenomena. The study will combine theory, algorithm development, and computational modeling, with the goal of advancing scalable hybrid approaches for next-generation fluid simulations. Who we