40 phd-mathematical-modelling-ecological-modelling Fellowship positions at University of Michigan
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Required Qualifications* PhD degree in bioinformatics Minimum 1 year of relevant experience Hands-on experience with R and/or Python and performing independent computational modeling. Ability to maintain
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the mathematical and statistical properties of the model in order to achieve higher agreement with biological assumptions and better interpretability. The candidate will have strong expertise in generative AI
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Qualifications* PhD Degree in Engineering, Computer Science, Data Science, Applied Mathematics, Statistics, or a related field Familiarity with (biomedical) signal processing Experience working with clinical data
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, biomedical engineering, applied mathematics, or related field. Working in team science. Desired Qualifications* The ideal candidate will have demonstrated experience in physical modeling of biological systems
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recruiting a post-doctoral researcher to develop computational models to study the spatial organization and microenvironment interactions in tumors using spatial multiomic data. The lab focuses on mathematical
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& Bioinformatics, is seeking candidates for a postdoctoral research fellow position. This is primarily for an NIH-funded project developing multimodal variational autoencoder models and probabilistic trajectory
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experiments to validate findings from the omic studies. The desired skill set is split between dry lab (bioinformatics) and wet lab (basic science/animal models). Additionally, the individual will be required
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) PhD graduate who is interested in strengthening their experimental, analytical, and publishing skills before transitioning to independence within academia or bio-pharma industry. Transferring
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of engineered tendon models Quantify and analyze inflammatory signaling in tendon fibroblasts Investigate ECM stiffness effects on cellular behavior and gene expression Develop fluorescent reporter systems
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. vector design and generation, genetic murine models, and/or genome editing), a strong understanding of physiology and disease models, as well as a track record of innovative scientific contributions