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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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While deep learning has shown remarkable performance in medical imaging benchmarks, translating these results to real-world clinical deployment remains challenging. Models trained on data from one
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The proposed PhD project aims to build a machine learning/deep learning-based decision support system that provides recommendations on precision medicine for paediatric brain cancer patients based
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We are seeking a motivated PhD candidate to work on unsupervised music emotion tagging within the broader field of affective computing. The project aims to develop reproducible machine learning
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Europe Marie Skłodowska-Curie Actions Doctoral Network (MSCA DN) COMBINE. The successful candidate will undertake research on: Deep learning for solidification in multiphase flows with radiative heat
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engineering or similar. Knowledge and experience with deep learning models applied in computer vision. Remarkable academic trajectory, validated by a strong record of publications in relevant international
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. • Conducting novel, publishable research related to machine learning, deep learning, reinforcement learning, and planning. • Development and maintenance of Python software and packages. • Drafting
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identification framework. The role involves developing a species-agnostic computational pipeline that integrates cutting-edge deep learning methods - such as AlphaFold2 and FoldSeek -to enhance protein function
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addition to teaching duties, the PhD candidate is expected to conduct research in the field of (deep) machine learning, with applications in either biomedical image understanding (e.g., surgical video analysis in
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learning to discover the chemical rules of cellular accumulation, and 2) establishing a high-throughput precision mutagenesis pipeline to functionally annotate chemically tractable amino acids on new drug