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Field
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-Coding Genome Edits: Develop innovative machine learning approaches for designing precise non-coding genome edits, focusing on how non-coding alterations influence gene regulation and cellular function
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-sensor hyperspectral data for crop disease detection and monitoring. Develop machine learning or physics-informed models to retrieve reliable spectral and/or biophysical features, from multi-scale
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remodeling. We employ advanced computational and theoretical techniques, such as large scale flow network simulations, machine learning, and methods from topological data analysis, to a broad set of problems
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optimization of multi-modal LLMs. Investigate and implement methodologies to ensure AI authenticity, accountability, and the integrity of digital content. Develop and refine machine learning and deep learning
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4 Sep 2025 Job Information Organisation/Company Instituto Pedro Nunes Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions Bachelor Positions
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Australian National University | Canberra, Australian Capital Territory | Australia | about 1 month ago
candidate will join Associate Professor Jean Wen’s research group, conducting independent, high-impact research at the intersection of AI/machine learning, computational biology, and RNA biology. The position
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transferring learning from other geographic regions and data types, machine learning methods, Bayesian inference and interrogation theory. The post may involve travel to Iceland and Italy in support of your work
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that machine learning applications are developed with ethical considerations in mind. Participate in regular meetings with the research group. Required Qualifications* Ph.D. in Electrical Engineering, Computer
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team. Preferential factors: academic performance, with a focus on Machine Learning and Biomedical sciences previous experience (e.g., research, professional, lecturing) in the domains of the grant
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of these features for distinguishing attacks from normal behaviour, through statistical and/or machine learning-based analysis. • Analyse the applicability and potential adaptation of these features for anomaly