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% Experimental Design and Implementation: Design and perform experiments to assess the regulatory roles of histone PTM-interacting proteins in transcription and associated processes. Develop and optimize genomic
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2 Apr 2026 Job Information Organisation/Company Aalborg Universitet Department The Technical Faculty of IT and Design, Department of Computer Science, Section for Human Centered Computing Research
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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hemocompatible coating strategies to improve membrane–blood interactions. - Model and optimize membrane performance using computational tools, machine learning, and artificial intelligence Work Plan - Synthesis
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developing learning-based perceptive approaches. You will develop learning and/or optimization based approaches to multi-robot path and motion planning and coordination, integrating multi-modal perception and
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deep learning. You will support the development of an improved forest RTM that can exploit LiDAR full-waveform data along with hyperspectral signatures. You will plan and carry out field campaigns in
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research achievements. The University’s unique applied learning pedagogy integrates work and study, embedding authentic learning experiences within real-world environments. Through strategic partnerships
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advancing them for testing in biopharmaceutical production. The successful candidate will be expected to work on the identification, verification, and design of ultraviolet spectroscopy and machine learning
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Center for Biologics Evaluation and Research (CBER) | Southern Md Facility, Maryland | United States | 4 days ago
interactions by investigating the molecular pathways that govern host defense responses and microbial persistence. Learn to design, train, and iteratively refine machine learning models to learn distinguishing
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predictive control of carbon mineralisation through high-throughput mineralogy and machine learning.” This is an exciting opportunity to contribute to innovative research at the interface of mineralogy