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Field
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/Fall 2026). Job Description: The Visual Cognition Lab studies how humans perceive, interpret, and navigate real-world scenes, linking visual information, semantic inference, and task demands to behavior
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data · Demonstrated expertise in causal inference and high-dimensional risk adjustment/predictive modeling · Clear scientific writing and communication, an ability to work both independently and in teams
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completed all requirements for the Ph.D. by the start date) • Strong training in causal inference and empirical research methods • Proficiency in Stata, R, Python, or similar Preferred
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quantitative methods to draw causal inferences from non-experimental data, using existing data linkages available in CREATE. These include linkages of registry data across several decades, as well as registry
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., feature engineering, spatiotemporal modeling, Bayesian calibration, ensemble methods) to improve prediction accuracy and uncertainty quantification. Disseminate research findings through presentations
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/deployment. Causal Inference/Experimentation: Knowledge of experimental design, randomization, and causal identification methods. There are no teaching requirements for these open positions. Basic
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to explore the evolution of taxa and characters and to infer centres of species/phylogenetic diversity and –origin, as well as dispersal trajectories. The candidate will work in a research team with several
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. Explore whether the real-world evidence (RWE) approaches as well as Bayesian borrowing can help address sample size and ethical concerns for rare diseases. Collaboration between the statistician at DB9 and
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polarization (apical–basal and planar) cytoskeletal architecture (actomyosin, microtubules) and mitotic spindle organization Map mechanical forces using visual mechanical stress inference (VMSI) and direct
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such as: Causal inference and the design and analysis of experiments Reinforcement learning and sequential decision-making Analysis of complex systems, networks, and large-scale data Machine learning