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Concurrent Materials Design, Accelerated by AI
This article references topics presented by Dr. Michael Heiber at Enthought’s 2025 R&D Innovation Summit in Tokyo. Link...
Data Pipelining & Augmentation
Step-by-step automation of the ingestion of data from multiple sources, following distinct stages and enriched with provenance and context.
Workflow Automation & Redesign
Targeted automation tools designed and built to remove manual and inefficient workflows, so scientists can focus on the science.
Scientific Data Management Systems
Secure, centralization of scientific data from siloed and fragmented sources for improved data integrity, collaboration, and insights.
Data Capture Systems
Standardization of how data enters your ecosystem for increased reliability and usage, decreased errors, and higher quality downstream analytics.
High Volume Data Management
Data management tools capable of handling big data, turning your vast amounts of scientific data from overwhelming burden to competitive advantage.
Database Design
Databases with structured schemas and indexing intentionally designed for the complexity of scientific data and R&D workflows.
Data Engineering
Designing the systems that collect, store, and process large volumes of raw data to ensure the data is usable and reliable for analysis and decision-making.
Core Technologies
Machine Learning, Deep Learning, Bayesian Optimization, Generative Adversarial Networks, Graph Neural Networks
Advanced Modeling & Systems
Reasoning Models, Multi-Scale Modeling, Surrogate Modeling, Simulation, Image Processing, Agentic AI Systems
Language & Generative AI
Natural Language Processing, Foundation Models, Generative AI, Large Language Models