Brookhaven National Laboratory (www.bnl.gov) delivers discovery science and transformative technology to power and secure the nation’s future. Brookhaven Lab is a multidisciplinary laboratory with seven Nobel Prize-winning discoveries, 37 R&D 100 Awards, and more than 70 years of pioneering research. The Lab is primarily supported by the U.S. Department of Energy’s (DOE) Office of Science. Brookhaven Science Associates (BSA) operates and manages the Laboratory for DOE. BSA is a partnership between Battelle and The Research Foundation for the State University of New York on behalf of Stony Brook University. BSA salutes our veterans and active military members with careers that leverage the skills and unique experience they gained while serving our country. Our organization fully supports service members transitioning from active duty to civilian life and pledge’s our commitment to actively hire veterans of the U.S. Armed Forces. Military personnel who have been formally trained or have relevant experience obtained while in service may meet educational requirements and are encouraged to apply for job opportunities at BSA.

The mission of the Instrumentation Division is to develop state-of-the-art instrumentation required for experimental research programs at Brookhaven Lab and to maintain the expertise and facilities in specialized high technology areas essential for this work. Development of facilities is motivated by present Brookhaven research programs and anticipated future directions of research. Our work has a significant impact on programs throughout the world that rely on state-of-the-art radiation detectors and readout electronics.

We seek an individual to assist design and testing of artificial neural networks, especially spiking neural networks (SNN), for classification of pulses generated in a sensor by different radiation particles. The SNN will be used to enhance the performance and efficiency of radiation detectors based on novel sensors and ASIC electronics. The detectors under development target a broad range of application areas, requiring beyond-state-of-the-art performance in sensitivity, compactness, and environmental robustness. Examples include instrumentation for the upcoming Electron-Ion Collider (EIC), the ATLAS experiment at the LHC, NSLS-II and other synchrotron light sources, and a variety of programs in Nuclear Nonproliferation and medical imaging. The ideal candidate will also be required to put in practice design of a portion of hardware in a CMOS process proving implementation of the SNN on-a-chip.

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  Master degree in Computer Science or Electrical Engineering or related discipline, plus 1 years’ post Masters experience or enrolment in PhD program
• Understanding of artificial neural networks, especially spiking neural networks, and their applications to signal processing and classification
• Familiarity with high-throughput data architectures and associated software frameworks
• Knowledge of scientific computing using C++, Python, or similar environments
• Willingness to work in a project-structured environment with defined tasks and milestones
• Background in CMOS circuit design for neural network implementation
• Demonstrated ability in integrating state-of-the-art microelectronic components into instruments tailored to specific application needs