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to understand how biodiversity and ecosystem functioning are linked and changing across different habitats in response to climate change and/or human use of natural resources. The successful candidate will
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in practice. You will also work alongside a diverse team of researchers and stakeholders from different disciplines and sectors, gaining valuable experience in interdisciplinary collaboration and
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survival and growth in naturally regenerating woodland plots subjected to different livestock grazing pressures. Activities include: (i) conducting targeted literature reviews to support the research
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will be grounded in rigorous mathematics coupled with a sound understanding of the underlying earthworm ecology. Bayesian inference methodologies will be developed to estimate where and when behavioural
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industrial and regulatory partners, ensuring its relevance and applicability in practice. You will also work alongside a diverse team of researchers and stakeholders from different disciplines and sectors
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individual rates of ageing. Role You will extend BrainAGE from global estimates to regional normative models using Bayesian regression and GAMLSS to derive age- and region-specific reference distributions
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) for engineering systems and structures, as well as expertise in machine learning, stochastic modeling, and Bayesian statistics. Programming Skills: Proficiency in programming languages such as Python, C, or R
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, stochastic modeling, and Bayesian statistics. Programming Skills: Proficiency in programming languages such as Python, C, or R. Teamwork and Responsibility: Ability to work effectively within a project team
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environment, of engineering AI solutions to problems (especially neural networks or large language models) and/or applying data science techniques (such as Bayesian or similar statistical modelling). You should
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project is to develop scalable and privacy-preserving Bayesian computational algorithms. The position is intended for two to three years, with an initial one-year appointment renewable contingent upon