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Field
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research areas include, although not limited to, the design of clinical trials, observational studies, longitudinal analysis, survival analysis, epidemiologic modeling, Bayesian analysis, modern causal
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version control and containerization (Docker/Singularity) Statistical Modeling: Quantitative data analysis using GLMs, Bayesian methods, or mixed-effect models to interpret complex perturbation datasets
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of electromagnetic wave physics or astrophysics, considered an asset. - Experience with advanced statistics and Bayesian inference, which will be regarded as a plus. Familiarity with compressed sensing and the ability
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experimental methods. Develop and apply methods for demultiplexing, normalization/QC, effect-size estimation, biological inference, and predictive modeling. Contribute to biological manuscripts and methods
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and manipulating complex data structures, Bayesian modeling, analyzing nested longitudinal data, and who are familiar with techniques for handling challenging data (e.g., highly non-normal distributions
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., Merilä, P., Vanhatalo, J. & Laine, A.-L. (2024) Inferring ecological selection from multidimensional community trait distributions along environmental gradients. Ecology, 105(9): e4378. Doi: doi.org
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. Ecology Letters, 28(4): e70003. Doi: 10.1111/ele.70003 Kaarlejärvi, E., Itter, M. S., Tonteri, T., Hamberg, L., Salemaa, M., Merilä, P., Vanhatalo, J. & Laine, A.-L. (2024) Inferring ecological selection
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, surveys, experiments, simulations, Bayesian inference, and advanced quantitative analysis. We are especially interested in courses on the applied use of generative AI, including courses on developing and
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 2 months ago
deep learning theory and practice. Applicants with expertise in probabilistic modelling, approximate inference, deep learning, or Bayesian optimisation are encouraged to apply. Interpretable Machine
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the graduate curriculum, and social sciences scholarship across the school. Examples of topic areas include (but are NOT limited to): models for inference (e.g., SEM/CFA, Bayesian modeling, linear mixed effects