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Field
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optimisation; uncertainty and sensitivity analysis; and machine learning or AI-supported optimisation. Strong analytical and programming skills are essential. Relevant experience may include tools and languages
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. The qualified candidate will design and lead projects grounded in data analysis in the domain of computational pediatric cancer research. They will develop and support data processing and analysis tools, create
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for Science, and scientific discovery by advancing our understanding of interpretable machine learning models and their practical applications in real-world domains such as healthcare and science. Key
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. Proficiency in programming tools such as Python, C++, and machine learning frameworks is expected, together with strong communication, organizational, and collaborative skills. Experience in infrastructure
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bioinformatics research focused on cancer epigenomics, gene expression, single-cell and spatial omics, and metabolomic data analysis. Develop computational tools, apply AI and machine learning approaches
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Nair, to study how machine learning models and neural circuits in the brain process states of emotion. The Nair group combines cutting-edge systems neuroscience tools with the development of new AI
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Challenge grant. The successful candidate will work closely with the Principal Investigators (PIs) to develop and implement innovative research integrating machine learning, computer vision, and wildlife
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artificial intelligence and machine learning (AI/ML) models that predict therapeutic response, identify clinically actionable patient subgroups, and support personalized treatment strategies. Through
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interdisciplinary team of molecular biologists, bioinformaticians, physicists, and pathologists to develop a biophysically interpretable machine learning model that integrates diverse biological data—including
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, Educational Technology, Learning Sciences, Human-Computer Interaction, or a related discipline. Strong interest in research relating to Generative AI, AI literacy, digital learning, workplace learning, or human