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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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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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approaches. Experience with tabular foundation models or foundation models for structured data is particularly relevant to this position. Key qualifications include: Strong research experience in deep learning
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missions operated by LATMOS. The postdoc will employ deep learning approaches using satellite data and ground stations. -Understanding the infrared data from the IASI mission and identifying the channels
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 5 hours ago
Demonstrated research experience in machine learning, deep learning, medical image analysis, computer vision, biomedical data science, or a closely related area. Strong programming skills in Python and
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learning and physics, addressing key challenges in modern quantitative biology. The successful candidate will be responsible for: • Develop and train deep learning models (CNNs, ...) data to predict IPLSs