Postdoctoral Researcher

Job Description

Position Overview SPIRE (Signal Processing Interpretation and REpresentation) Lab in the Department of Electrical Engineering, Indian Institute of Science, Bengaluru, seeking a highly motivated and talented Postdoctoral Fellow to join our research group under CV Raman Postdoctoral Fellowship to work at the intersection of acoustics, computational physics, and machine learning. The selected candidate will focus on the development and application of physics-informed neural networks (PINNs) for solving forward and inverse problems in acoustics, including time-harmonic and time-domain wave propagation, impedance characterization, and boundary value problems. Key Responsibilities Develop robust and scalable PINN architectures for simulating acoustic wave propagation in 1D, 2D, and 3D geometries. Formulate physics-constrained loss functions using the governing equations of acoustics (e.g., Helmholtz and wave equations). Implement boundary conditions including Dirichlet, Neumann, and Robin types for complex domains. Collaborate with team members on high-performance computing (HPC) or GPU-accelerated implementations of the models. Analyze results and validate models using analytical, numerical, or experimental benchmarks. Publish research findings in peer-reviewed journals and present at international conferences. Assist in mentoring graduate and undergraduate students working in related projects. Required Qualifications Ph.D. in Acoustics, Applied Mathematics, Mechanical/Aerospace Engineering, Computational Physics, or related fields. Strong background in numerical methods (FEM, FDM, BEM, etc.). Experience with deep learning frameworks such as TensorFlow or PyTorch. Familiarity with physics-informed machine learning (PINNs, DeepONets, etc.). Proficiency in scientific computing using Python; knowledge of GPU acceleration is a plus. Good publication record in relevant fields. Strong communication and collaborative skills. Preferred Qualifications Experience in solving forward problems in acoustics. Prior work involving sound wave propagation modeling in complex or inhomogeneous media. Exposure to uncertainty quantification or hybrid physics–data-driven modeling. Duration 2 years (extension subject to year-wise performance) How to Apply Interested applicants should upload the following documents for the initial screening using the link: https://forms.gle/2WEPGS8rfbWhjoFd8 Cover letter detailing relevant experience and research interests CV including list of publications