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Field
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microbial communities. In this role, you will develop hybrid species distribution models that combine climate and landscape data to predict how microbial taxa niches shift under changing land use and
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modern Bayesian modelling frameworks such as Stan, Turing.jl, and PyMC, including automatic differentiation frameworks, MCMC sampling algorithms, and iterative Bayesian modelling. Special attention will be
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the utility and the robustness of different explanation strategies. A large focus of this project will be on leveraging novel and interpretable approaches in applied domains such as algorithmic fairness and
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characterization of deep-water habitats, GIS spatial analysis of species distribution data, and quantification of ecosystem services. Preference will be given to applicants that possess a diverse set of skills and
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to scientific publications. Operates, maintains, and troubleshoots standard laboratory equipment. Organizes laboratory stock, maintains inventory, and distributes supplies as needed. May assist in training and
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standard laboratory equipment. Organizes laboratory stock, maintains inventory, and distributes supplies as needed. May assist in training and onboarding new staff and assigning tasks to employees at lower
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for new quantum computing algorithms. It will rely on statistical structure learning represented by knowledge graphs and efficient low-rank tensor compressions. We are looking for: A completed scientific
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to these challenges, working with high performance and distributed computing environments, working with large-scale machine learning models, and a proven research record of scholarly contributions through publications
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developed in discussion with the recruited candidate and the team, the main question of the project is the extent to which alterations to chromatin distribution and methylation arise, and how they contribute
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integrating local flexibility markets through distributed AI-based coordination, market mechanism design, and cloud-to-edge computing. It aims to develop scalable machine learning methods for coordinating grid