Biography

I am an AI/ML Scientist and Engineer with expertise in building scalable machine learning frameworks and deploying AI systems on high-performance computing (HPC) and cloud environments. My work combines applied mathematics, numerical modeling, and modern AI to solve large-scale, real-world problems.

At the University of Western Australia, I develop machine learning models for climate science and oceanography, including predictive AI systems for marine heatwave detection and downscaling climate forecasts. Previously, at the University of Luxembourg, I worked on wind engineering problems. I designed machine learning and deep learning solutions for wind engineering and CFD applications. During my Ph.D. at the Technical University of Berlin, I developed reduced-order modeling techniques and AI-driven algorithms for financial risk analysis, combining machine learning, deep learning, and complexity reduction methods for stochastic and high-dimensional systems.

I have extensive experience working with servers, distributed HPC systems, and cloud-based workflows (AWS/GCP), enabling large-scale training, optimization, and deployment of machine learning models. My technical skills span Python, MATLAB, TensorFlow, PyTorch, Scikit-learn, MLflow, FastAPI, Git, Docker, and Linux environments, with strong expertise in MLOps practices and automation pipelines.

I am passionate about applying AI to critical domains including climate science, finance, and engineering. My interests include deep learning, diffusion models, numerical optimization, reduced modeling, and intelligent systems for data-driven decision-making.

Interests
  • Machine Learning
  • Artificial Intelligence
  • Computational Mathematics
  • Model Order Reduction
  • Computational Fluid Dynamics
  • Climate Science
  • Computational Finance
  • Stochastic Analysis
  • Optimization
  • High performance computing
Education
  • PhD in Mathematics, 2022

    Technical University of Berlin

  • MSc in Computational Science and Engineering, 2018

    University of Rostock

  • BEng in Mechanical Engineering, 2015

    University of Pune

Skills

Python

Advanced

C++

Basic

MATLAB

Advanced

TensorFlow

Deep Learning

PyTorch

Deep Learning

Keras

Neural Networks

Scikit-learn

ML Models

MLflow

Experiment Tracking

FastAPI

APIs

Git

Version Control

Docker

Containers

GPT

LLMs

ChatGPT

Generative AI

Artificial Intelligence

AI & Automation

Machine Learning

Algorithms & Models

Deep Learning

Neural Networks

Diffusion Models

Generative Models

MLOps

Deployment & CI/CD

Finance

Risk & Stochastic Systems

Climate Science

Climate & Ocean Modeling

HPC & Linux

Supercomputing & Shell

Cloud (AWS/GCP)

Deployment

Experience

 
 
 
 
 
Research Fellow
University of Western Australia
Nov 2023 – Present Perth, Australia
  • Developed a novel Residual Corrective Neural Network (RCNN) framework for statistical downscaling of sea surface temperature (SST).
  • Enhanced spatial resolution of SST forecasts from 25 km to 2 km, significantly improving accuracy for marine heatwave detection.
  • Achieved high-resolution downscaling with reduced computational cost compared to traditional numerical models.
  • Leveraged HPC clusters and servers for large-scale training and optimization.
  • Collaborated with oceanographers and climate scientists to integrate AI frameworks into operational forecasting pipelines.
  • Published results in peer-reviewed journals and presented at international conferences.
 
 
 
 
 
Postdoctoral Researcher
University of Luxembourg
Jan 2022 – Sep 2023 Luxembourg
  • Built a digital twin of urban canopies using ML and CFD integration, supporting sustainable and green city design.
  • Developed ML models that predicted wind energy potential with 98% accuracy and operated 20x faster than CFD simulations.
  • Applied physics-informed neural networks (PINNs) for fluid dynamics and structural mechanics problems.
  • Delivered AI-driven solutions for renewable energy resource management and urban comfort studies.
  • Mentored graduate and PhD students on ML frameworks and deployment practices.
 
 
 
 
 
Researcher
Technical University of Berlin
May 2020 – Dec 2021 Berlin, Germany
  • Engineered ML and reduced-order modeling frameworks for financial risk simulations.
  • Designed algorithms that executed 10,000 simulations within seconds for both mobile and desktop banking applications.
  • Applied deep learning and stochastic modeling to accelerate high-dimensional financial risk analysis.
  • Collaborated with MathConsult GmbH to align AI-driven tools with industry requirements.
  • Contributed to publications and delivered technical presentations at finance and ML conferences.
 
 
 
 
 
Researcher
MathConsult GmbH
Nov 2018 – Apr 2020 Linz, Austria
  • Developed efficient machine learning frameworks for portfolio risk analysis and simulations.
  • Preprocessed and analyzed large-scale interest rate and financial datasets.
  • Applied surrogate modeling and ML algorithms to improve accuracy and reduce computational cost.
  • Delivered insights and technical reports to banking clients and stakeholders.
 
 
 
 
 
Research Assistant
Collaborative Research Center ELAINE, University of Rostock
Oct 2017 – Sep 2018 Rostock, Germany
  • Applied ML and nonlinear PDE solvers to optimize thermoelectric generator (TEG) design for medical implants.
  • Conducted simulations for thermo-fluid systems and optimized TEG efficiency under varying environmental conditions.
  • Implemented nonlinear machine learning models to accelerate electrothermal simulations.
  • Contributed to cross-disciplinary R&D in medical device engineering.
 
 
 
 
 
Teaching Assistant
University of Rostock
Oct 2017 – Sep 2018 Rostock, Germany

Responsibilities:

  • Tutor two courses as a tutor involving numerical methods for PDEs, linear algebra, and compact modeling (model order reduction).
  • Supervised and guided students on their software projects.
  • Designed and conducted exams.

Selected Publications

  • Deep learning-based statistical downscaling of sea surface temperature using a residual corrective neural network
    O. Jadhav, M. Rayson, T. French, I. Janekovic, N. Jones – Forthcoming Manuscript, 2025

  • A multi-fidelity wind surface pressure assessment via machine learning: a high-rise building case
    A. Šarkić Glumac, O. Jadhav, V. Despotović, B. Blocken, S.P.A. Bordas – Building and Environment, 2023

  • Error analysis of a model order reduction framework for financial risk analysis
    A. Binder, O. Jadhav, V. Mehrmann – ETNA, 2022

  • Model order reduction for the simulation of parametric interest rate models in financial risk analysis
    A. Binder, O. Jadhav, V. Mehrmann – Journal of Mathematics in Industry, 2021

📖 See full list on Google Scholar

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