BNL’s Electronic Detector Group (EDG) in the Physics Department at BNL has great strength in the study of the physics of flavor and an extensive history in kaon, muon and neutrino experiments and recent research in heavy flavor physics with the Belle II experiment. EDG physicists have major responsibilities in several neutrino experiments: the Deep Underground Neutrino Experiment (DUNE) and its prototype (ProtoDUNE), the short-baseline neutrino program (SBN includes MicroBooNE, SBND, and ICARUS), Daya Bay and PROSPECT. The EDG currently has twenty-five physicists at various career levels and a long history of research in fundamental particle physics. In collaboration with the Instrumentation Division, the group performs research and development in electronics and detector design for liquid-argon TPCs (LArTPC). In collaboration with the BNL Chemistry Department, the group performs research and development on water-based liquid scintillator and reactor neutrino experiments. In collaboration with the Computational Science Initiative (CSI), we develop new software and computing tools to advance intensity frontier research.

Position Description:

The Physics Department has an opening for a postdoctoral researcher to take a leading role in developing novel Machine Learning models to minimize systematic differences between simulations and real data. The position is in EDG and will be working closely with Machine Learning experts in CSI. In addition, opportunities exist to participate in various related research topics carried out by EDG including involvement in current and designing future LArTPC experiments such as MicroBooNE, ProtoDUNE, ICARUS/SBND and DUNE. The selected candidate will have the opportunity to broaden impact by applying these techniques to additional experiments, such as the sPHENIX experiment at the Relativistic Heavy Ion Collider, a major new detector under construction at BNL. This position has a high level of interaction with the international and multicultural scientific community. A review of the applicants will start on January 4, 2021 and continue until the position is filled.

Essential Duties and Responsibilities:

• Lead role in developing and applying novel machine learning techniques

• Use advanced simulations and real detector output to prepare data sets for machine learning

• Validate results to improve simulations and the machine learning techniques

Required Knowledge, Skills, and Abilities:

• PhD in high-energy particle or nuclear physics or related fields

• Familiarity with machine learning fundamentals, techniques and applications

• Strong programming ability and in particular, proficiency with modern C++ and Python

• Experience in HEP/NP offline software development

• Clear and concise written and interpersonal communication, and analytical skills Preferred Knowledge, Skills, and Abilities:

• Understanding of Fourier analysis, convolutions, linear algebra

• Experience in HEP/NP physics analysis and working on collaborative software projects

• Use and development of particle detector event simulation and reconstruction software

• Experience with advanced machine learning techniques such as GANs

• Familiarity with ML software systems such as PyTorch, TensorFlow as well as Python’s scientific software stack (numpy)

• Familiarity with software engineering practices that include testing, documentation, source code management, and release procedures

• Use of individual and clustered computing with Linux-based operating systems

• Experience with high-performance computing and heterogeneous computing (e.g. MPI, OpenMP, OpenACC and/or CUDA or message passing systems such as based on ZeroMQ)

Other Information:

• BNL policy requires that research associate appointments be made to individuals who have received their doctorate within the past five years.

• Initial two-year term appointment subject to renewal contingent on performance and funding

Brookhaven National Laboratory (BNL) is an equal opportunity employer committed to ensuring that all qualified applicants receive consideration for employment and will not be discriminated against on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, status as a veteran, disability or any other federal, state or local protected class. BNL takes affirmative action in support of its policy and to advance in employment individuals who are minorities, women, protected veterans, and individuals with disabilities. *VEVRAA Federal Contract