137 software-engineering-model-driven-engineering-phd-position positions at Brookhaven Lab
Sort by
Refine Your Search
-
, architectural, and civil services to build, support and maintain the Lab's infrastructure. Position Description As part of the Civil of the Facilities and Operations Directorate, our Multi-Trade Rigging
-
, architectural, and civil services to build, support and maintain the Lab's infrastructure. Position Description As part of the Civil of the Facilities and Operations Directorate, our Rigging Supervisor is
-
candidates should have a major in electrical engineering, computer science, or applied mathematics. A background in electric power systems modeling and simulation and data analytics and machine learning
-
Organization Overview The Facilities & Operations (F&O) Directorate supports Brookhaven National Laboratory’s (BNL) science, technology, and environmental restoration missions by delivering safe
-
engineering; (ii) Video Foundation Model and its scientific and security applications. The position provides access to world-class computing resources, such as the BNL Institutional Cluster and DOE leadership
-
relevant business role which provides evidence of the particular knowledge, skills and abilities necessary to perform successfully the duties of the position. Experience preparing detailed data driven
-
reduction and other chemical transformations, and the radiolysis of non-aqueous media that are used in energy applications, e.g., ionic liquids and alkyl carbonates. The position will make heavy use
-
The Facilities & Operations (F&O) Directorate’s mission is to support the science and technology and environmental restoration missions of the Laboratory by providing a safe, environmentally sound
-
security, research security training, and export control training, as appropriate. This position reports to the Lab’s Deputy Director for Science & Technology. BNL’s existing Export Control Office will
-
Nanomaterial Discovery, integrating synthesis, advanced characterization, physical modeling, and computer science to iteratively explore a wide range of material parameters. The CFN develops and utilizes