I’m Karan Anand, PhD — a CFD engineer who spends most of his time simulating particles that are much too small to care, but crucial enough to power the next generation of clean energy. My background spans gas-solid multiphase flow simulations, high-performance computing, and the delightful intersection of applied physics and machine learning.

Computational engineer with over 5 years of experience designing and implementing advanced numerical algorithms and high-performance simulations. Expert in applied mathematics techniques such as quaternion-based rotational dynamics, optimisation methods, and large-scale parallel computing. Skilled programmer in Python, Fortran, and C++, with extensive experience managing simulations on national HPC infrastructures (Adastra, Jean-Zay, TGCC, CALMIP). Passionate about developing robust, scalable, and efficient computational frameworks that combine physics-based modelling with machine learning to solve complex engineering problems. Proven ability to collaborate across disciplines and deliver tools that bridge theory, simulation, and experimental data.

Now, I’m looking for exciting career prospects at the intersection of Physics, engineering, and machine learning — especially those that let me push the boundaries of simulation and data-driven modeling. If that’s your thing too, let’s talk.