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An exciting opportunity has arisen for two Research Assistants (RAs) within the Institute of Mental Health. The RAs will be working on the ESRC/NIHR funded DETERMIND programme (www.determind.org.uk
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School of Technical Sciences, Aarhus University, Denmark, within the Quantitative Genetics and Genomics programme. The position is available from 01 January 2026 or later. You can submit your application
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computational and machine learning approaches to integrate Oxford Nanopore (ONT) long-read data with bulk and single-cell RNA-seq profiles. The aim is to identify host-microbiome molecular signatures that drive
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of the largest Statistics departments in the world, with a thriving PhD programme and a long-established reputation for research activity of the highest quality, enhancing both the PhD student experience and the
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mechanisms and dynamics Chemistry » Solar chemistry Chemistry » Structural chemistry Chemistry » Computational chemistry Engineering » Chemical engineering Engineering » Materials engineering Physics
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neurophysiological recordings, imaging studies, and behavioral experiments Present research findings at internal seminars, external conferences, and scientific meetings Actively engage in the Neuroscience PhD Program
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21st September 2025 Languages English English English The Department of Computer Science has a vacancy for a PhD Candidate in Human Building Interaction Apply for this job See advertisement This is
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, computer science, medicine, pharmacology, and physics. ISAS is a member of the Leibniz Association and is publicly funded by the Federal Republic of Germany and its federal states. At our location in
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 1 day ago
of Contract Temporary Job Status Part-time Offer Starting Date 15 Oct 2025 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number 54/08/25 Is the Job
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an ARC Linkage Project focused on developing an autonomous system for detecting and quantifying structural damage in infrastructures (e.g., bridges, grain silos) using computer vision, digital twins, and