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Field
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these operations, and then train transformer models to predict sequences of operations in unseen data. Please see Weindel, Borst, & van Maanen, eLife, 2025, Weindel, van Maanen, & Borst, Imaging Neuroscience, 2024
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Outer Membrane Vesicles (OMVs) from attenuated, genetically modified Salmonella strains; - Key activities involve applying reverse vaccinology pipelines (in silico epitope prediction and 3D molecular
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responses. Structural Bioinformatics & Biophysical Modeling: Protein structure prediction (e.g., AlphaFold, RoseTTAFold, ESMFold), molecular dynamics (MD) simulations, or protein-protein/antigen-antibody
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prediction models Demonstrated ability in program management, collaboration, and stakeholder engagement Position Sponsorship Eligibility: Not eligible for work visa sponsorship About Department of Crop and
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between moisture uptake, glass transition temperature (Tg), and material performance, Development of predictive durability and service-life models for infrastructure applications including rehabilitation
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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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(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