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, methodologies, and information derived from Bayesian modeling, data science, cognitive science, and risk analysis. Its primary objective is to create advanced forecasting models, generate meaningful indicators
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, linear mixed modelling, time series analysis, causal inference). Experience working with large, multimodal and/or open access data sets Interpersonal and communication skills toeffectively collaborate and
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from chickpea roots. Integrate a range of relevant transcriptomics datasets to infer spatiotemporal gene regulatory networks of suberin deposition. Test predicted network interactions between MYB
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, methodologies, and information derived from Bayesian modeling, data science, cognitive science, and risk analysis. Its primary objective is to create advanced forecasting models, generate meaningful indicators
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or statistics, with a special preference for those with expertise in one or more of the following areas: representation learning, causal inference. We are interested in attracting a PostDoc who is able to perform
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(or earlier/later, if mutually agreed). The position is intended for an economic historian with expertise in causal inference using quasi-experiments and an interest in studying political preferences and racial
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), and physiological parameters in the study of animal behaviour; a strong background in data analysis using R, preferably experience with Bayesian statistics and social network analysis; lab experience
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developments in the field and either push the boundaries of SLT on the mathematical foundational theory side, extend SLT to new learning frameworks (e.g. variational inference or reinforcement learning, etc