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Field
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discussions that drive the lab’s work. We also see postdocs as important mentors and role models for graduate students in the Krishnan lab, helping elevate the scientific thinking and rigor of the group
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computational methods to address and derive theoretical models and predictions. For this line of research we are seeking several postdoctoral researchers to work synergistically both within the team and with our
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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and
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. Preferred Qualifications: Experience in decision analysis and developing computer-based simulations to model either infectious or non-infectious diseases. Evidence of research productivity in mathematical
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assimilation frameworks for high-dimensional dynamical systems. Strong skills in modeling, simulation, and Monte Carlo analysis for aerospace systems. Ability to integrate estimation algorithms into closed-loop
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related discipline): Computer Science, Artificial Intelligence, or Machine Learning Economics or Econometrics (particularly applied micro, behavioural, or decision-focused modelling) Applied Mathematics
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bring Essential PhD Degree in a relevant scientific field (e.g. computer science, data science, mathematics, engineering, or related); Strong understanding of generative models (e.g., VAEs, GANs
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mathematics, or a related field. Experience in modelling community dynamics; prior work with size- or stage-structured models is preferred. Publication record commensurate with career stage. The working
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biological and quantitative techniques including microscopy, flow cytometry, computational/mathematical modeling, and utilizes approaches from synthetic biology and complex biological systems. Highly motivated
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challenges in stem cell research, aging and disease modeling. The group employs methodologies from different areas of mathematics, engineering, and physics, and integrates multiple sources of biological