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following areas is desirable: collection and analysis of dense nodal seismic datasets, ambient noise tomography, high-performance computing, machine learning applications in observational seismology, and
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astrophysics, cosmology, or a related field completed by the start date; strong programming skills; working knowledge of machine learning applied to astrophysics and cosmology, in particular simulation-based
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stimulation (TMS), and transcranial direct current stimulation (tDCS) through interdisciplinary collaborations. Behavioral measurements and physiological recordings, including facilities for computer-based
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mechanics, machine learning or applied/computational mathematics. ● Demonstrated ability to carry out original mathematical derivations and/or develop computational tools, evidenced by publications
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against simpler machine-learning baselines; • train and evaluate ARCA on large-scale microbiome datasets, with attention to sparsity, batch effects, scalability, generalisation across studies and
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will join a research group working on longitudinal models of multiple chronic diseases across the life course. The postdoctoral researcher will contribute to building and evaluating machine learning
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their added value against simpler machine-learning baselines; train and evaluate ARCA on large-scale microbiome datasets, with attention to sparsity, batch effects, scalability, generalisation across studies
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experience through research, internships, and industry-sponsored capstone projects. With a versatile curriculum spanning software, systems design, nanofabrication, and machine learning, the program prepares
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. Olga Fink ). IMOS develops advanced machine-learning and AI methods for complex engineering and industrial systems, with a particular focus on improving their reliability, availability, and operational
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global scales; national security and critical minerals governance; principles and norms for mitigating environmental and social impacts from mining; and institutional design and practices for addressing