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- Planeamento de tratamento biológico robusto do cancro pancreático em radioterapia”, “2024.14662.PEX”, “DOI: https://doi.org/10.54499/2024.14662.PEX ”, funded by the Fundação para a Ciência e a Tecnologia, I.P
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include: Lead original research in multimodal and causal AI for health; design, implement, and rigorously evaluate algorithms and full pipelines. Build reproducible research pipelines and maintain reliable
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an emphasis on technology, data science and the humanities. For more details, please view https://www.ntu.edu.sg/medicine . We are looking for a Research Fellow to conduct AI for medicine research. The role
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exam. Desired qualifications: Experience with data simulation, clustering algorithms, benchmarking, model selection and evaluation workflows is an advantage Language requirement: Good oral and written
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to machine learning algorithms in order to get uncertainty estimates for parameters governing the distribution of the observed data. The predictive Bayes scheme for uncertainty quantification contains a wide
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 22 hours ago
expected to develop and run workshops to introduce your work to end users. For further detail on the Lanfear Lab, our ideals and goals please visit our webpage: http://biology.anu.edu.au/research/labs
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, particularly Python; (ii) use of parameter optimization algorithms, particularly PEST and PEST++; (iii) remote sensing applied to the water cycle; and (iv) application of machine learning techniques to spatio
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. - Contribute to the development of risk-prediction tools, biomarker panels, and precision-medicine algorithms. - Participate in NIH-funded translational studies involving spatial multi-omics, proteomics
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. Responsibilities include conceptualizing and implementing statistical and structural models, developing scalable algorithms for system optimization and control, conducting policy-relevant economic analysis
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of Computation Group, seeks applicants for a postdoctoral fellowship to conduct research in differentially private learning, its connections to replicability of algorithms, and algorithmic fairness. Basic