Postdoctoral Fellowship in Physics-Informed AI, Soft Matter Physics, and Biology
The International Centre for Theoretical Sciences (ICTS) in India is inviting applications for a fully funded postdoctoral fellowship under the CALIBRE AI in Biology Postdoctoral Fellowship Program. This opportunity is for candidates interested in working at the intersection of physics-informed artificial intelligence, machine learning, soft matter physics, and biological systems. The project will be supervised by Dr. Rituparno Mandal (Raman Research Institute) and Dr. Brato Chakrabarti (ICTS).
Research directions include, but are not limited to: (1) physical learning in biology-inspired flow networks, exploring nonlinear hydrodynamics and computation in complex flow systems; (2) biology-inspired soft material design using machine learning, focusing on adaptive and responsive meta-materials; and (3) active matter and AI-enabled control of living systems, applying AI/ML tools to analyze and predict collective dynamics in living matter.
The fellowship offers a competitive salary of up to ₹1.25 lakh per month, with an additional ₹3 lakh per year for research and travel expenses. The program is designed as a 2-year immersive experience at ICTS, providing deep cross-disciplinary exposure across artificial intelligence, physics, and biology.
Applicants should have a PhD in a relevant field (such as physics, AI, machine learning, or related disciplines), with strong computational skills in fluid mechanics, physical learning, and dynamical systems. Experience with high-performance computing (HPC) is required, and prior experience with GPUs is advantageous but not mandatory.
To apply, candidates should submit a cover letter, CV, and a short research proposal to [email protected] by March 1, 2026. For informal queries, applicants may contact [email protected] and [email protected]. More details are available at the ICTS opportunities page.
This position is ideal for researchers interested in interdisciplinary work at the cutting edge of AI, physics, and biology, with a focus on computational modeling, generative models, and machine learning for living systems.
