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We are seeking a postdoctoral researcher to join an interdisciplinary team developing deep learning models for antimicrobial resistance (AMR) detection directly from MALDI-TOF mass spectrometry data
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Apply now » Internal Research Fellow (PostDoc) in Theory of Deep Learning Job Requisition ID: 20829 Date Posted: 5 August 2026 Closing Date: 2 September 2026 23:59 CET/CEST Publication: External
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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 1 hour ago
and fluid properties in the subsurface formations. Therefore, the objectives of this research project include two aspects: (1) based on the cutting-edge technologies from deep learning, computer vision
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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 1 hour ago
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 Fellow
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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 1 hour ago
that fosters professional growth and development. Qualifications A Ph.D. in Computer Science, Chemical Engineering, Materials Science, or a related field, with a strong focus on deep learning
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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 1 hour ago
graphics, and deep learning. A suitable candidate should have publications in good venues, mainly CVPR, ICCV, ECCV, ICLR, NeurIPS, Siggraph, and Siggraph Asia. Various research topics are available and
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focuses on algorithm development, data fusion, and deployment for anomaly detection in solar panels and battery systems. Key Responsibilities Develop computer vision and deep learning algorithms
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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 1 hour ago
based on deep learning to accelerate the physics simulation for membrane relevant processes, which can be based on physics-informed neural network, data-driven models, and hybrid simulation models
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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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experience with the PyTorch library and running deep learning models. The successful candidate will work closely with a team of researchers and faculty members in the ClinicalNLP lab led by Dr. Hua Xu