AI / Python Engineer

AI / Python Engineer Needed for Physics-Informed Neural Network (PINN) Integration with Nanobubble Water Treatment Systems

Project Overview
 We are seeking an experienced AI / Machine Learning engineer to help us integrate Artificial Intelligence into our nanobubble generator systems. The goal is to use AI to predict water quality changes and system performance once our units are installed in real-world environments.
We have extensive laboratory and field data (before and after installation) that will be used to train and validate the model. The project will involve developing Physics-Informed Neural Networks (PINNs) to model water parameter behaviour and system outcomes.

Core Objectives
Develop AI models to predict water quality changes after nanobubble system installation
Use Physics-Informed Neural Networks (PINNs) to incorporate known physical and chemical water dynamics
Predict the number of nanobubble units required per project based on site-specific conditions
Integrate external environmental data such as weather, tides, and seasonal variations to improve prediction accuracy
Create a scalable framework that can be reused for future projects and installations

Data & Inputs
Historical lab and field data (before/after nanobubble treatment)
Water quality parameters (e.g., DO, ORP, turbidity, COD,BOD etc.)
Environmental data sources (weather, tidal, and possibly flow data)
System configuration data (number of units, operating parameters)

Technical Requirements
Strong experience with Python
Proven experience with Machine Learning and Deep Learning
Hands-on experience with Physics-Informed Neural Networks (PINNs)
Experience working with time-series and environmental data
Familiarity with data integration from external APIs (weather, tidal data)
Ability to design, train, validate, and document predictive models

Nice to Have
Background in water treatment, hydrodynamics, or environmental engineering
Experience with scientific modelling or hybrid physics/ML systems
Visualization and reporting of model outputs

Deliverables
Trained PINN-based predictive model(s)
Codebase in Python with documentation
Model validation and performance analysis
Guidance on scaling and future improvements

Project Type
Fixed price
Potential for long-term collaboration as the system evolves
Please include examples of relevant projects, especially involving PINNs, environmental modelling, or physics-based AI systems.
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