POSTDOCTORAL ASSOCIATE, Kavli Institute for Astrophysics and Space Research, to join the High Energy Physics and Laser Interferometer Gravitational-wave Observatory groups. Will work on the application of machine learning algorithms, heterogeneous computing combining CPUs, GPUs, as well as field programmable gate arrays (FPGAs) to real-time data acquisition, triggering, and physics science analyses. The two groups are collaborating on these aspects as part of an NSF award. Seek someone who can bring modern machine learning strategies combined with heterogeneous computing to the science frontier both at the Compact Muon Solenoid (CMS) detector on the Large Hadron Collider (LHC) and at the LIGO observatory. Will be part of a team to design and deliver a GPU/FPGA based acceleration system to be used for rapid gravitational wave detection, and high-speed processing of CMS data on the LHC.
REQUIRED: a Ph.D. in physics, astronomy, or computer science; and familiarity with machine learning accelerated approaches and their applications to scientific problems. PREFERRED: experience with machine learning algorithms and with GPUs and FPGAs. Job #18200
The position is available effective immediately and applications will be considered until the position is filled; but to ensure full consideration, candidates should apply by December 1, 2019.
In addition to applying online with a curriculum vitae via the MIT careers portal ( https://careers.peopleclick.com/careerscp/client_mit/external/jobDetails/jobDetail.html?jobPostId=17287&localeCode=en-us) , candidates are asked to arrange for three letters of recommendation to be sent via email to Philip Harris (firstname.lastname@example.org) and Erik Katsavounidis (email@example.com).
The appointment is initially for one year and renewable annually.