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Science in Earth Observation develops innovative signal processing and machine learning methods, and big data analytics solutions to extract highly accurate large-scale geo-information from big Earth
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design, train and implement ARCA: an AI foundation model for crop microbiomes. You will work at the interface of deep learning, bioinformatics and microbial ecology, using large-scale microbiome and genome
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of the land on which we live, work and create The Inclusive Design Research Centre seeks a researcher/developer at the post-doctoral or PhD candidate level to join an interdisciplinary team co-designing and
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as a member of the GHER contributing to the EU research project COMEDI in a consortium of 11 leading partners in the field of data assimilation and deep learning. A successful applicant will develop
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expertise in large language models, foundation models, or modern AI systems and they will possess sufficient specialist knowledge of LLMs, foundation models, deep learning, NLP, mechanistic interpretability
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, linear mixed models, or deep learning architectures). A solid theoretical background in literacy development, child language acquisition, or psycholinguistics. A record of academic publishing and high
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, you will design, train and implement ARCA: an AI foundation model for crop microbiomes. You will work at the interface of deep learning, bioinformatics and microbial ecology, using large-scale
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be involved in the three-year project “High Dimensional Hierarchical Optimization methods for Machine Learning and Stochastic Optimal Control”. Background or expertise in one or more of the following
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. Under the supervision of Luke Johnson, PhD and Jerrold Vitek, MD PhD, the associate will work collaboratively within the NMRC on an existing project supporting the collection and analysis of large-scale
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Electrocatalysis who has: a PhD in chemical engineering, chemistry, materials science, physics or a closely related discipline, completed by the start date deep understanding of electrochemistry/electrocatalysis and