54 deep-learning Postdoctoral positions in Ireland-University-Ranking-2024 in Denmark
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postdoctoral researcher to join an interdisciplinary team developing deep learning models for antimicrobial resistance (AMR) detection directly from MALDI-TOF mass spectrometry data. The project is funded
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At AAU Energy, a position as Postdoc in AI and Deep Learning for Radar-Based Non-Destructive Testing is open for appointment from 01.10.2026 or as soon as possible hereafter. The position is
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, extract, and standardise functional information 2. Develop computational tools that integrate evolutionary and functional information using comparative genomics and deep learning approaches 3. Apply
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: Overcoming Inequity in Embodied Learning in Danish Vocational Education and Beyond, funded by Independent Research Fund Denmark. This is a full-time (37 hours per week), fixed-term (24 months) postdoctoral
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. The project focuses on the intersection of deep reinforcement learning, probabilistic modeling, and bio-inspired architectures (such as Spiking Neural Networks) to achieve sample- and energy-efficient robust
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of Prof. Georg Madsen, with regular shorter research stays at Aarhus University. The project combines density functional theory (DFT), machine-learned force fields and atomistic simulations to uncover how
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to recruit a talented researcher for a 2-year, full-time postdoc in machine learning from 1 October or soon thereafter. Your work tasks We are looking to recruit an excellent postdoctoral fellow to apply
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genetic basis of plant–microbe interactions, with a particular emphasis on data integration across plant species and data types (genomics, transcriptomics). Design, adapt and use deep learning methods
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developing new visualisation strategies to aid delineation, as well as developing deep learning methods to enhance photon-counting CT images and better visualise tissue boundaries. The project will also
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development and deployment via deep learning approaches (SE3D)” which is a collaboration between Head of Center and Professor Martin Bøgsted, Center for Clinical Data Science, Aalborg University, Professor