Intern – Quantitative and Computational Modeling

Job Title:

Intern—Quantitative & Computational Modeling Location:

Bengaluru-Rajarajeshwari Nagar / Onsite Organization:

MetFlux Research Private Limited Employment Type:

Internship (with potential conversion to full-time based on performance)

About MetFlux Research MetFlux Research is building the

ΩmeX ecosystem

— a biology-first intelligence platform that integrates

systems biology, multi-module analytics, and quantitative modeling

to power precision health, personalization, and predictive R&D. Our modeling and analytics team supports: MetSights

– practitioner-grade precision insights based on blood, gut, urine, and lifestyle data MetSolve

– in-silico, physiology-aware modeling for health and wellness innovation This internship is designed for candidates who want to work at the intersection of

biology, mathematics, data, and real-world health applications .

Role Overview As an

Intern – Quantitative & Computational Modeling , you will work closely with senior scientists and modelers to build, validate, and interpret

data-driven and mechanistic models

that support MetFlux’s ΩmeX ecosystem. You will gain hands-on exposure to: multi-modal health data integration physiological and pathophysiology-driven modeling analytical and computational pipelines used in real production systems This role is ideal for

fresh graduates

who are analytically strong, curious about biological systems, and motivated to apply quantitative skills to meaningful health problems.

What You Will Work On Your work will directly contribute to: Data integration pipelines

across blood, gut microbiome, urine metabolomics, and clinical data Multi-modal physiological networks

and pathway-level representations Data-driven, ML-assisted, and mechanistic models

for different pathophysiological states Validation of

MetSights personalized insights

using quantitative and biological checks

Key Responsibilities Integrate and process multi-modal biological and clinical datasets for modeling workflows. Develop, simulate, and evaluate quantitative or computational models under guidance. Translate biological mechanisms and pathophysiology into

mathematical or algorithmic representations . Validate models and pipelines both

analytically (data/statistics)

and

biologically (mechanism plausibility) . Document models, assumptions, datasets, and outputs in alignment with MetFlux standards. Support senior scientists in building

physiology interpretation layers

for the ΩmeX knowledge graph and MetSights modules.

Who We’re Looking For Education Completed

MTech / MS

(or final-year students) in: Computational Biology Bioinformatics Biotechnology (with strong quantitative focus) Applied Mathematics / Engineering (with biology exposure)

Core Competencies Strong understanding of

computational and mathematical modeling concepts . Good working proficiency in

MATLAB and Python . Ability to analyze, visualize, and interpret diverse data types. Sound

statistical foundations

for data analysis and inference. Ability to read and interpret

scientific literature , especially mechanistic and clinical studies. Clear scientific writing and structured documentation skills. High attention to detail, reproducibility, and data quality.

Nice to Have Ability to break down complex modeling pipelines into smaller, interpretable modules. Exposure to

machine learning

in biological or health datasets. Familiarity with

Git , notebooks, or basic automation workflows. Curiosity-driven mindset with evidence-based reasoning.

Why Join Us Competitive internship stipend. Hands-on experience in a

real-world precision health and modeling program . Close mentorship from senior scientists working on systems biology and quantitative modeling. Learn how

data, biology, and computation

come together in applied health intelligence. Opportunity for

full-time conversion

based on performance and team fit.

How to Apply Send your CV (and an optional short note on your interest) to: talent@metflux.in Subject Line:

Intern – Quantitative & Computational Modeling

along with applying to this job post.