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Field
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resources while addressing workflow requirements for scientific applications. Validate distributed intelligence algorithms at scale on ORNL's computational resources, including the Frontier supercomputer
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, manage, and run daily, monthly, and quarterly reporting packages, with a keen eye on data quality and robustness, and distribute them to relevant parties within the office and at DFA. Identify critical
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objects, by embedding them into a 2 or 3-dimensional space through a representation learning algorithm, has been widely used for data exploratory analysis. It is particularly popular in areas such as
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processing algorithms from concept to implementation. Eligibility to obtain a United States SECRET clearance (or higher) is required for ongoing employment in this position Minimum Qualifications: 1
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the values are drawn independently from known distributions. These results have been widely applied in areas such as auction theory, resource allocation, and stochastic optimization. The interdependent value
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distributed intelligence across the computing continuum. In this role, you will have the opportunity to lead and contribute to cutting-edge research aimed at transforming scientific data management and
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methodologies in brain diseases. The candidate will work on developing advanced new algorithms, testing and validation, and applications in these data modalities. The candidate will have the opportunity to work
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) to specify use cases from the perspective of the relevant domains as well as model transformation, analysis, and synthesis algorithms that provide functionally correct system configurations with a guaranteed
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | about 2 months ago
to compare base approaches (no active learning) with different state-of-the-art active learning algorithms including our solutions; Participate to the redaction of a publication exposing the methodologies and
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federated knowledge graph framework that facilitates the querying, consolidation, analysis, and interpretation of distributed proteomics-focused clinical knowledge graphs. To achieve this, we will employ