120 phd-studenship-in-computer-vision-and-machine-learning positions at Brookhaven Lab
Sort by
Refine Your Search
-
and external scientists. Consistently demonstrates very high levels ingenuity and creativity. Research is recognized as important by sponsoring DOE program manager(s). Develops new and original research
-
. · Contribute to writing procedures to ensure operational excellence. Required Knowledge, Skills, and Abilities: · BA/BS Degree or equivalent experience, preferably in Computer Science or related technical
-
are conducted in compliance with BSA’s Prime Contract, applicable statutes and regulations, and in accordance with the highest ethics and standards. Oversight of BNL Property Management Program to effectively
-
guidelines for programming and documentation Required Knowledge, Skills, and Abilities: BA/BS Degree or equivalent experience, preferably in Computer Science or related discipline 1 + year(s) of relevant
-
performance measurements. Essential Duties and Responsibilities: You will lead the acquisition and commissioning of a milliKelvin scanning probe microscope at the CFN. You will lead a research program on
-
also hosts one of the brightest x-ray synchrotrons in the world, the Center for Functional Nanomaterials, the quantum information science Center for Co-Design for Quantum Advantage, the NASA Space
-
cost plans, analysis of current year cost, rate development, and preparation of financial summaries for management, periodic program reviews, and Laboratory planning exercises. Required Knowledge, Skills
-
. To support this program, the NSLS-II must design, fabricate, assemble, test, install, and operate unique research equipment to generate, transport, transfer, accelerate and focus electron and X-ray beams
-
. To support this program, the NSLS-II must design, fabricate, assemble, test, install, and operate unique research equipment to generate, transport, transfer, accelerate and focus electron and X-ray beams
-
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