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Field
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-derived cells and single-cell perturb sequencing to establish foundational models to predict the effects of potential drug candidates on cardiovascular diseases. By combining genome engineering, functional
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05.07.2026, Academic staff Our research combines mathematical modeling, numerical simulation, scientific computing, and data-driven methodologies to improve the predictive capabilities and
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ingredients. The working group Molecular Modeling / Section III is looking for 1 post-doctoral fellow, to start on 01/12/2026 The position is to be filled for a period of 2 years. Research project Advancements
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distribution systems. Current research initiatives include developing advanced vibration analysis methodologies for wooden packaging systems, predictive modeling for packaging performance, and optimization
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: Comparative genomics RNA secondary structure prediction Covariance models and RNA homology search methods Machine learning or artificial intelligence applied to biological data Transcriptomics analysis methods
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time, building and validating ML/AI-based prediction models, creating field zones tied to management actions supported by farm economics, and developing scaling-up solutions for different management
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 2 months ago
substantial intellectual independence and opportunities to collaborate closely with machine learning scientists developing predictive models of cellular function. Machine learning experience is not required
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cloud cover may amplify rather than mitigate global warming but the magnitude of this is highly uncertain. One of the greatest challenges in climate science is to predict how clouds in general, and
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to develop predictive models for polymer-based materials. This project aims to leverage computational chemistry techniques and data-driven approaches to optimize the properties of novel polymer-based materials
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path-flows, and other key parameters and inputs. In your role as a postdoctoral researcher, you will: Integrate hybrid traffic models and data assimilation methods into a coherent prediction framework