Hyderabad, India | Hybrid | Full-Time

About Aganitha

Accelerate drug discovery and development for Biopharma and Biotech R&D with in silico solutions leveraging Computational Biology & Chemistry, High throughput Sciences, AI, ML, HPC, Cloud, Data, and DevOps.

In silico solutions are transforming the biopharma and biotech industries. Our cross-domain science and technology team of experts embarks upon and industrializes this transformation. We’re continually expanding our world-class, multi-disciplinary team in Genomics, Computational Modeling, AI, and Cloud Computing to accelerate drug discovery and development. What drives us is the joy of working in an innovation-rich, research-powered startup bridging multiple disciplines to bring medicines faster for human use. We are working with several innovative Biopharma companies and expanding our client base globally. Read about how and what solutions we build.

Aganitha (अगणित): “countless” or “limitless” in Sanskrit serves as a reminder and inspiration about the limitless potential in each one of us. Come join us to bring out your best and be limitless!

Role Overview

Aganitha is looking for a Multi-scale Modeling Scientist. The core of this role involves “scaling up”, taking molecular insights and accurately representing them at the mesoscopic and continuum levels.

You will be responsible for the sophisticated simulations required to simplify the complex molecular systems without losing essential chemical accuracy. By bridging the gaps of resolution, you will enable everything from design of stable drug delivery vehicles to high-performance consumer formulations.

Key Responsibilities

• Perform advanced computational simulations: e.g., Coarse-Grained (CG) modeling and parameterization, Langevin Dynamics (LD), Dissipative Particle Dynamics (DPD), Brownian Dynamics (BD) or bridge the resolution gap with Continuum modeling using SCFT (Self-Consistent Field Theory), Computational Fluid Dynamics (CFD), Lattice Boltzmann method (LBM), Phase Field Modeling, Finite Element Methods (FEA, FFEA, FEM), etc.) to understand phase behaviour, self-assembly, stability, and complex design principles relevant to various hard and soft matter systems e.g., Lipids, polymers, surfactants, colloids, RNA, proteins, etc.
• Develop and refine CG force fields using advanced systematic mapping techniques, potential derivations using Iterative Boltzmann Inversion (IBI), Force-Matching, Bayesian Optimization (BO), and such methods
• Analyse and interpret data from multi-scale trajectories to create descriptors for AI/ML models, aiming to predict bulk system behaviour from mesoscopic structures.
• Understand, analyze, critique, and implement research papers, tailoring approaches to specific problem contexts.
• Develop clear and concise narratives of data-driven analyses performed using computational techniques.
• Effectively articulate and communicate complex domain knowledge to cross-functional teams. Participate actively in requirements gathering, design discussions, and demonstrations.
• Continuously learn and stay up-to-date on emerging technologies and scientific advancements in computational chemistry, formulation science, and related fields—research opportunities for applying advanced computational methods to evolving industry challenges.

Required Qualifications

• PhD or Post-Doc in Computational Chemistry, Chemical Engineering, or Physics, with a strong emphasis on Statistical Mechanics and Multi-scale Modeling.
• Direct Experience in systematic coarse-graining or continuum modeling.
• Domain Knowledge: Proven track record in modeling soft matter (polymers, colloids, lipids, proteins) or complex industrial fluid mixing processes.
• Demonstrated first-hand research experience in problems in the domain of multi-scale modeling, for example:
  • CG / DPD / ultra-coarse-grain methods modeling of lipids, ionic liquids, or polymer assemblies.
  • Modeling of fluid flow mixing using Computational Fluid Dynamics or other phase-field methods (e.g. mixing of LNP formulation containing RNA and lipids in aqueous buffer)
  • Interactions between various components in a formulation, e.g., surfactant or polymer interactions with diverse substrates (e.g., skin, hair, fabric) and shear simulations.