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Call for Proposals: Get on Big Iron with the ALCF Data Science Program

The ALCF Data Science Program at Argonne has issued its Call for Proposals. The program aims to accelerate discovery across a broad range of scientific domains which require data-intensive and machine learning algorithms to address challenging research problems. “Ongoing and past ADSP projects span a diverse range of science domains, e.g. Materials, Imaging, Neuroscience, Engineering, Combustion/CFD, Cosmology; and involve large science collaborations.”

Call for Proposals: ALCF Data Science Program

Argonne is now accepting proposals for the ALCF Data Science Program (ADSP) through July 1, 2019. “The ADSP open call provides an opportunity for researchers to submit proposals for projects that will employ advanced statistical, machine learning, and artificial intelligence techniques to gain insights into massive datasets produced by experimental, simulation, or observational methods.”

Data Science Program at Argonne Looks to Machine Learning for New Breakthroughs

Over at Argonne, Nils Heinonen writes that four new projects for the ALCF Data Science Program that will utilize machine learning, deep learning, and other artificial intelligence methods to enable data-driven discoveries across scientific disciplines. “Each project intends to implement novel machine learning techniques; some will integrate these methods with simulations and experiments, while others will pioneer uncertainty quantification and visualization to aid in the interpretation of deep neural networks.”

Argonne’s Data Science Program Doubles Down with New Projects

Today Argonne announced that the ALCF Data Science Program (ADSP) has awarded computing time to four new projects, bringing the total number of ADSP projects for 2017-2018 to eight. All four of the program’s inaugural projects were also renewed. “The new project award recipients include an industry-based deep learning project; a national laboratory-based cosmology workflow project; and two university-based projects: one that uses machine-learning for materials discovery, and a deep-learning computer science project.”