Postdoctoral Researcher – AI-Driven Scientific Computing

Amrita Vishwa Vidyapeetham; Bengaluru; Presencial; INR 1.800.000 - 3.000.000; Jornada completa

Descripción de la vacante

Amrita Vishwa Vidyapeetham invites applications for a postdoctoral researcher position in Bengaluru. The role focuses on applying mathematical modelling and machine learning to PDE-driven problems, with emphasis on PINNs, neural operators, and physics-informed data-driven simulations.

The candidate will work at the intersection of Applied Mathematics and Scientific ML, exploring scalable algorithms, surrogate modeling, and AI-assisted computational workflows within a collaborative university

Formación

• Ph.D. in Mathematics, Applied Mathematics, Computational Science, Computer Science, AI, Engineering, Physics, or a related field.
• Minimum h-index of 5 at the time of application.
• Strong publication record in SCI/SCIE-indexed journals.
• Experience in AI/ML, SciML, PINNs, Neural Operators, or related computational AI.
• Proficient in Python and/or MATLAB; familiarity with PyTorch, TensorFlow or JAX is advantageous.
• Knowledge of PDEs, numerical methods, and scientific computing.

Responsabilidades

• Develop AI-driven methodologies for solving complex PDEs and multiscale problems.
• Advance PINNs, Neural Operators, and other SciML frameworks for forward/inverse problems.
• Collaborate on numerical methods, computational modelling, and HPC workflows.

Conocimientos

Python

MATLAB

PyTorch

TensorFlow

JAX

HPC

PDEs

Scientific computing

Educación

PhD in Mathematics/Applied Mathematics/Computational Science/CS/Engineering

Herramientas

NumPy

SciPy

FEM

FEniCS

Descripción del empleo

Company Description: Amrita Vishwa Vidyapeetham is a multi-campus, multi-disciplinary research institution recognized among the leading research universities in India. With eight campuses, the university offers a broad and collaborative environment for academic and research careers. Amrita maintains active partnerships with top universities in the United States, including Ivy League institutions, and leading European universities, enabling international collaboration and exchange. The institution is known for its rapid growth in higher education and strong emphasis on cutting-edge research, making it an attractive destination for postdoctoral researchers seeking impactful work and global exposure.

Role Description: The selected researcher will work at the intersection of Applied Mathematics, Scientific Machine Learning, Artificial Intelligence, and Computational Engineering, developing advanced AI-driven methodologies for solving complex scientific and engineering problems governed by partial differential equations (PDEs). The research will primarily focus on integrating machine learning and deep learning architectures with mathematical modelling, numerical methods, and scientific computing. Particular emphasis will be placed on developing and implementing Physics-Informed Neural Networks (PINNs), Neural Operators, and other emerging scientific AI frameworks for forward and inverse problems, predictive modelling, and computational simulations.

Key Research Areas:

• Scientific Machine Learning (SciML): Physics-informed machine learning, scientific deep learning, and hybrid physics-data-driven models.
• Physics-Informed Neural Networks (PINNs): Forward and inverse PDE problems, parameter estimation, and data assimilation.
• Neural Operators: Fourier Neural Operators (FNO), Deep Operator Networks (DeepONet), and related operator-learning architectures.
• Artificial Intelligence and Machine Learning: Deep learning, surrogate modelling, regression, predictive analytics, and intelligent optimization.
• AI-Assisted Scientific Computing: LLM-assisted scientific workflows, automated computational modelling, and AI-enabled numerical simulations.
• Computational Mathematics: Numerical PDEs, Finite Element Methods (FEM), numerical analysis, and high-performance computing (HPC).
• Multiphysics and Engineering Applications: Computational fluid dynamics, heat and mass transfer, transport phenomena, biomedical modelling, and digital twins.
• Advanced Computational Methods: Inverse problems, uncertainty quantification, sensitivity analysis, and data-driven optimization.

Eligibility and Requirements:

• Ph.D. in Mathematics, Applied Mathematics, Computational Science, Computer Science, Artificial Intelligence, Engineering, Physics, or a closely related discipline.
• A minimum h-index of 5 is mandatory. Applicants must satisfy this criterion at the time of application.
• A strong publication record in reputed international, peer-reviewed journals, preferably in SCI/SCIE-indexed journals.
• Demonstrated research experience in one or more of the following: Artificial Intelligence, Machine Learning, Deep Learning, Scientific Machine Learning, PINNs, Neural Operators, or related computational AI methodologies.
• Strong programming skills in Python and/or MATLAB. Experience with frameworks such as PyTorch, TensorFlow, or JAX will be an advantage.
• Knowledge of mathematical modelling, numerical methods, PDEs, and scientific computing will be highly desirable.
• Experience in implementing computational algorithms and developing independent research contributions is expected.