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: Token compression and adaptive token pruning; Distributed and collaborative inference strategies; Mixture-of-Experts (MoE) architectures for scalable inference; Resource-aware and latency-constrained
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soon as possible thereafter. Job description The position is part of the SNSF-funded project “AI-driven comparative QTLomics to accelerate crop improvement,” a collaborative project with
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) to collect, extract, and standardise functional information 2. Develop computational tools that integrate evolutionary and functional information using comparative genomics and deep learning
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Join us at the Department of Electrical and Computer Engineering at Aarhus University for a two-year postdoctoral position focused on physics-driven machine learning for ground-based, airborne, and
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machine learning methods and pipelines. Qualifications Ideally, you hold a PhD in Data Sciences and have several years of experience collaborating with scientists in natural sciences, particularly
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collaborative research into the molecular and genetic basis of plant–microbe interactions, with a particular emphasis on data integration across plant species and data types (genomics, transcriptomics). Design
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data visualization, with a track record of designing and building visualization dashboards. Research experience in human-centered AI, or in the integration of AI and machine learning methods into
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structural data streams. Strong documented skills in advanced statistics, AI and machine learning methods, including demonstrated experience in analysing multiple global change drivers, e.g. land use
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and machine learning methods—including large language models—into the visualization pipeline. Responsibilities Lead research projects within the intersection of data visualization, human-centered AI
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regular shorter stays in Aarhus and online collaboration. Experience with one or more of the following will be considered an advantage: active-learning strategies for force-field construction, Green