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Tier-1 brand and Bangalore metro increase competition, but niche PhD semiconductor+ML skillset limits applicants.
Role requires deep semiconductor device physics and compact-model expertise, reducing cross-industry transferability.
PhD plus specialized semiconductor modeling, Verilog-A, and ML framework expertise make filters highly stringent.
Develop, enhance, and maintain compact semiconductor device models (CMOS, BJT, LDMOS) for SPICE simulators.
Validate and correlate models against device measurement data and resolve simulation convergence issues.
Design and implement deep-learning based methods and automation workflows for parameter extraction and model optimization.
Ph.D. in Semiconductor Device Physics, Characterization, Modeling, or related field.
Proficiency in semiconductor device physics, circuit simulation, and industry-standard compact models such as BSIM4, BSIM-BULK, PSP, or Mextram.
Experience with optimization algorithms, data correlation, and automated parameter extraction processes.
Work Experience Required: Not explicitly mentioned in the JD.
Strong expertise combining semiconductor device physics with AI/ML techniques for modeling and automation.
Technical skills in Python, Verilog-A programming, and machine learning frameworks like PyTorch, TensorFlow, or JAX.
Ability to operate collaboratively across global teams linking process development and circuit design functions.