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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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theoretical advances into practical insight and tools that can support analysis, design and decision-making for AI-enabled space systems, thereby bridging the emerging scientific theory of deep learning with
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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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areas of nanoscience and nanotechnology. Job Title: Junior Postdoc-Electrocatalysis; in situ electron microscopy characterization; deep learning frameworks for TEM image analysis Department: Advanced
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This is a unified application form for all positions in the Beyesian Deep Learning group at KAUST led by Prof Maurizio Filippone, including Research Intern MS/PhD Student PhD Student Postdoctoral
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project include two aspects: (1) based on the cutting-edge technologies from deep learning, computer vision or physics-informed machine learning, develop robust surrogate forward models to predict
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simulate a refined batch of synthetic data. For each batch, the postdoc will estimate Bayes optimal error, an important guide for realistic goals for deep learning. The first batch of synthetic data will be
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minimum qualifications at the time of hire. PhD in computer science, data science, or related discipline Track record of publications in Artificial Intelligence and Deep Learning in peer-reviewed
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25 Sep 2026 Job Information Organisation/Company University of Oslo Research Field Educational sciences Researcher Profile Recognised Researcher (R2) Positions Postdoc Positions Application Deadline
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high-dimensional biological data. Preferred Experience developing deep learning models for multimodal biomedical data. Experience with imaging, single-cell, spatial transcriptomics, electronic health