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Field
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proficiency in Python (e.g., NumPy, Pandas, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with supervised
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The lab of professor Jesper Tegnér at KAUST has openings for three postdoctoral fellowships in Data-driven Machine Learning for unbiased Discovery of Generative Models with special reference
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or translational research experience Knowledge of machine learning, Bayesian modeling, or statistical method development Ideal Personal Attributes: Independent, proactive, and scientifically curious Detail-oriented
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journals. Internal knowledge transfer, and collaborative research efforts. Develop and evaluate AI models, including, but not limited to, those for Multi-Agent Systems, Multi-Agent Reinforcement Learning
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publication record. Outstanding data analytics, mathematical, and computer modelling skills. Excellent interpersonal communication and oral presentation skills in English Self-driven and strong team spirit Open
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projects on metabolic diseases * Develop and apply machine learning models for biomarker discovery, patient stratification, and prediction of disease trajectories * Collaborate with clinicians
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share your findings through scientific publications and presentations. Additionally, you'll have the chance to teach or mentor students if you wish. Through our programs and collaborations with
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integration Key areas: § Metagenomic and 16S rRNA sequencing analysis § Single-cell RNA-seq and proteomic/metabolomic data integration § Machine learning and AI applications in microbiome data
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learning to model solid-state materials while collaborating with experimentalists. Qualifications • Ph.D. in Computational Physics/Materials Science, or related field (completed by start date
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processes. Preferred factors: Knowledge of Machine and Deep Learning; Knowledge in data exploration and processing; Knowledge of Generative AI models n mainly LLM's; Knowledge of satisfaction model analysis