Awarded Projects

The DYNAMITE (DYnamic biomolecular interaction Network And Machine learning Integration for Targeted Energy calculations) aims to revolutionize binding free energy (BFE) predictions by integrating large-scale molecular dynamics (MD) simulations with advanced machine learning (ML) methodologies.

This project proposes to run global convection simulations with GPU-accelerated MHD solver (Pencil Code - Astaroth; PC-A) to study these processes in an unprecedented parameter regime.

Phase-change processes are present in everyday life, nature, technology and in scientific applications. In particular, the melting of ice into salty water is directly linked to the melting of icebergs and glaciers into the ocean.

This proposed study aims to explore the effects of soluble surfactant contamination on heat transfer in a turbulent bubbly channel flow using our in-house high-fidelity front-tracking code.

Inspired by dual-system cognitive theories, the project proposes a hybrid framework where a lightweight, fast-acting model operates in real-time while a larger, high-capacity model predicts future representations and refines decision-making.

This research proposal outlines the development of a transformer-based methodology for generating long videos through diffusion modeling. Initially, we propose using a causal encoder to compress images and videos into a shared latent space, facilitating cross-modality training and generation.

Transport and handling of complex fluids consumes large amounts of energy worldwide, and improving their mixing and heat transfer crucial from process industry to medicine.

Hydrophobic gating occurs when the ionic current flowing through a nanopore is hindered by the reversible formation of a vapour bubble inside the pore.

Silicon is the most widely used semiconductor material in electronic industry, including photovoltaics. In spite of this dominance and many decades of research, important questions remain unsolved.