Nova In Silico is a health tech company that develops an in silico clinical trial platform jinkō to simulate drug efficacy and optimize clinical development using virtual patients and disease modeling. As an innovative company, we offer a dynamic work environment distinct from larger, established organizations. Interns will gain significant responsibilities and benefit from a steep learning curve, supported by a highly motivated team. Learn more at www.novainsilico.ai.

Keywords

Generative AI, Calibration, Optimization, QSP Modeling, Mechanistic Models, Explainable AI

Background

Nova In Silico is specialized in Quantitative Systems Pharmacology (QSP) Modeling, a computational approach that builds mechanistic, bottom-up models of drug-system interactions to predict outcomes in humans. Patient outcomes of treatments are evaluated by applying these models in silico to a population of virtual patients. A Virtual Population (VPop) is a set of simulated patients, each with a unique, biologically plausible parameter set, used to capture the patient-to-patient variability (heterogeneity) seen in clinical trials.

A calibration strategy in QSP modeling is the methodology used to select or adjust the internal model parameters to ensure the model’s output distribution accurately reflects the variability observed in clinical trial data.

Defining calibration strategies for these models is currently a manual, expert-driven process that is both time-consuming and difficult to reproduce. Recent advances in Generative AI offer a promising avenue to capture expert reasoning and suggest calibration strategies automatically.

This internship will explore the integration of AI into the calibration workflow, proposing strategies for which parameters to estimate, prior ranges, optimization methods, and justification of choices, optionally testing them on the Jinkō simulation engine.

Objective

Develop and prototype an AI-based assistant capable of proposing calibration strategies for mechanistic or QSP models, evaluate its performance, and integrate it into the existing calibration workflow.

You are

• A team player, a good listener, and an effective communicator
• Curious and proactive, ready to face real-life engineering challenges 
• Autonomous and self-motivated with strong analytical and problem-solving skills
• Eager to learn mathematical modeling and simulations of biological systems
• Willing to explore latest advances in science and technology
• Responsive and capable of tackling time-sensitive issues with agility

You will

• Review the scientific literature on hierarchical model calibration and AI-assisted reasoning
• Prototype AI strategies for calibration plan generation
• Evaluate against baseline strategies and document methods and results
• Integrate solutions into Nova’s simulation platform

Methodology and technical skills

We are looking for people who know some of the following or are eager to learn and work with them

A professional English level (written and oral) is required for this role.

Practical information