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(e.g., scRNA-seq) to investigate cellular heterogeneity, host-pathogen interactions, and immune responses. Structural Bioinformatics & Biophysical Modeling: Protein structure prediction (e.g., AlphaFold
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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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micro mobility applications. The primary focus will be on creating and optimizing state-of-charge (SOC) and state-of-health (SOH) prediction models to ensure the safety, efficiency, and longevity
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a postdoctoral researcher, you will: Integrate hybrid traffic models and data assimilation methods into a coherent prediction framework. Develop uncertainty quantification methods and explainable and
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 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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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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energy systems and demand response; process and energy system modeling; optimization and model predictive control; and applications of artificial intelligence and machine learning to energy systems
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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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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | about 2 months ago
and applying numerical models to derive the mechanical properties of irradiated materials from micromechanical measurement data, e.g., microstructure-based prediction of hardness profiles or extraction
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metadata, and clinical imaging workflows. Experience with image classification, segmentation, temporal modeling, representation learning, multimodal learning, or clinical prediction modeling. Familiarity