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special emphasis on a relaxed and cooperative working environment. Social interactions help facilitate active scientific exchange and foster a good atmosphere, and therefore play a big role in our team
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targets. You will take a lead on development of computational approaches to integrate multi-omics data from patient samples, including DNA methylation, histone modifications, single-cell transcriptomics and
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. They will also help coordinate and implement future field campaigns to fill key field data gaps. Additionally, the successful candidate will be responsible for advancing cutting-edge methods for quantifying
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team together with three PhD students to create a mutually supportive research environment. Candidates must be enthusiastic about building and working in a large interdisciplinary team. These positions
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, developing cutting-edge solutions for real-world challenges in financial services. You will lead and contribute to innovative research projects in machine learning, synthetic data, LLM orchestration, and RL
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Postdoctoral Research Associate in Forest Resilience, Climate Change, and Human Health in the Amazon
illnesses. The post holder will also co-supervise a PhD student who will be involved in the same project. This is a highly interdisciplinary project combining forest ecology, remote sensing, machine learning
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the areas: AI, deep neural networks, machine learning, applied topology, probability, statistics, signal processing. About the School The School has an exceptionally strong research presence across
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candidate with skills and experience in some of the following areas: Quantitative approaches such as longitudinal data analysis, analysis of large datasets and/or data science Experience of working with
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. Familiarity with standard design verification (DV) procedures and continuous integration (CI) setups would be beneficial. Knowledge of machine learning workloads and the design of machine-learning accelerators
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The post holder will develop computational models of learning processes in cortical networks. The research will employ mathematical modelling and computer simulation to identify synaptic plasticity