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Field
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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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dysbiosis drives immune dysregulation and disease progression in pediatric patients, generating new clinical multi-omics data and using deep learning, structural equation models, and causal inference
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interface of machine learning, deep learning, data science and applications in forest sciences. Together with the Director, you will further develop KIForst as a faculty-wide platform for methodological
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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