Research Fellow I

Job Description – Heterogeneous integration of CHIPLETS is turning out to be a gamechanger for future technology development and products in different applications including AI accelerators. The design and integration of CHIPLETS is a complex multi-physics task that requires substantial design space exploration and co-design and co-optimization accounting for the electrical, thermal and mechanical effects and their coupled interactions. In effect, this a complex problem that needs the application for AI / machine learning to enable guided, efficient and effective optimization of the CHIPLET performance leveraging also on reduced order models from high fidelity multiphysics simulation tools. This job position would involve the use of AI / ML for surrogate modeling, multi-objective optimization and inverse design as well as the use of reduced order models to aid in efficient design space search and optimization along the Pareto front.    

Job Skills – Candidate should have a solid background in microelectronics / materials science / electronic materials as well as experience in applying AI / machine learning techniques (both data and image-based) on interesting use cases as well as strong programming knowledge and experience with Python and its libraries that support ML work.

Job Requirements - Positions are immediately available for talented candidates with background in the above mentioned topics. The project will require a sound knowledge of machine learning coupled with material science, electronics packaging and thermo-mechanics / electrothermal coupled physics problems. Candidates should submit their resume / CV along with PDF copies of their relevant publications and scanned copies of their degree transcripts, course grades and certificates.