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Field
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to make a difference. Do you want to be involved and contribute to our development? Together, we can create a sustainable future through knowledge and innovation. We believe that knowledge and new
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resilience and its change over time in the past (based on Earth observation data), present and future (based on Earth system model simulations for different future scenarios, e.g. using the CMIP6 ensemble and
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, demonstrated experience of coding in programming languages such as R and Python is considered particularly advantageous. Examples of computationally intensive methods central to IAS and IDA are data-driven text
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resilience and its change over time in the past (based on Earth observation data), present and future (based on Earth system model simulations for different future scenarios, e.g. using the CMIP6 ensemble and
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) assess future changes in these patterns under different global warming scenarios. Requirements: The successful applicant should hold a MSc or PhD degree in physics, mathematics/statistics, climate science
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different numerical tools via Python scripting Experience conducting machine learning and inverse design models HPC experience Experience training and supervising students High proficiency in writing skills
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development. Experience with implementing statistical learning or machine learning (e.g. Bayesian inference, deep-learning). Programming skills in Python and experience with frameworks like PyTorch, Keras, Pyro
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making a difference to people's lives. We believe that inspiring our people to do outstanding things at Durham enables Durham people to do outstanding things in the world. Being a part of Durham is about
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optimize and ex-ante evaluate different components of an architecture for improving global public good provision that is sketched here . The architecture consists of two components, tax clubs and reward
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advanced AI/ML methods for robust analysis and integration. Data sparsity, batch effects, and missing values across different omics layers and platforms. Cross-omics data fusion and representation learning