Lab Partnering Service Discovery
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Title: HPC Application Architect
- Molecular dynamics
- Density Functional Theory Code Development
- Parallel programming (GNU parallel, MPI, OpenMP, PGAS models, etc.)
Hubertus (Huub) van Dam is a computational chemist with expertise in docking and molecular dynamics simulations. In prior work he has collaborated on improving the accuracy of docking calculations by using ab-initio molecular potentials for the electrostatic part of docking scores (DOI: 10.1063/1.2793399). He is currently supporting the National Virtual Biotechnology Laboratory (NVBL) effort to find COVID-19 drug candidates using Autodock 4.2, Dock 6 and DeepDriveMD. He also has extensive expertise in writing and supporting large parallel quantum chemistry packages. Currently, he serves as Testing and Assessment Task Lead on the Exascale Computing Project’s NWChemEx effort. NWChemEx is providing a community infrastructure for computational chemistry that takes full advantage of exascale computing technologies.
Energy research represents a major focus for BNL over the next decade. We are using a multifaceted approach driven by the unique state-of-the art laboratory facilities and the inter-disciplinary expertise of our scientific staff to solve fundamental questions regarding U.S. energy independence and to translate discoveries into deployable technologies. The laboratory has identified several energy focus areas – including biofuels, complex materials, catalysis, and solar energy.
BNL's one-of-kind user facilities include the National Synchrotron Light Source II NSLS-II, which produces extremely bright beams of x-ray, ultraviolet, and infrared light for scientists exploring materials—including superconductors, catalysts, geological samples, and proteins—to accelerate advances in energy, environmental science, and medicine. Scientists at our Center for Functional Nanomaterials create materials and explore their unique structure and properties at the nanoscale, with a focus on more efficient solar and energy storage materials. And at BNL's Northeast Solar Energy Research Center, where researchers from labs, academia, and industry study test new solar technologies, working to make solar "power plants" more efficient and economical
In addition to fundamental research, the laboratory actively collaborates with industry and other academic institutions to bring the benefits of scientific discoveries to the marketplace. Brookhaven's Office of Strategic Partnerships integrates Brookhaven Lab's industry engagement, technology licensing, and economic development functions to expand the impact of collaborative research and technology commercialization. Strategic Partnerships supports the Laboratory's science mission through identifying, pursuing and managing partnerships with a broad set of private-sector companies, federal agencies, and non-federal entities. For information on licensing and industry.
- Basic science: seeks to understand how nature works. This research includes experimental and theoretical work in materials science, physics, chemistry, biology, high-energy physics, and mathematics and computer science, including high performance computing.
- Applied science and engineering helps to find practical solutions to society’s problems. These programs focus primarily on energy resources, environmental management and national security.
Title: Assistant Computational Scientist
- Scientific literature processing to rapidly find protein-protein interaction candidates
- AI-based platform to accelerate discovery of novel drug compounds
- Querying and filtering interface for users to efficiently scan tens of thousands of sources
Since joining Brookhaven National Laboratory in 2018, Carlos X. Soto has been an active member of the Machine Learning Group, where he has contributed extensively toward large-scale scientific data extraction from published literature using natural language processing (NLP) techniques applied to areas such as functional genomics, drug discovery, and government reports. His contributions to machine learning for the integration of biological genomics data helped prompt an ongoing partnership with Oak Ridge National Laboratory to use NLP techniques to accelerate COVID-19 drug discovery. In 2019, Soto was part of the Brookhaven team awarded one of only two Nuclear Threat Initiative (NTI) Nuclear Security Index Challenge grants. The work, Towards a Predictive Nuclear Security Threat Model, aims to create a predictive model by integrating NTI Index data into machine learning and sentiment analysis. Presently, his work on COVID-related projects focuses on providing domain scientists with powerful new computational tools to identify patterns and insights in large volumes of documents, in particular relating to potential drug compound candidates.