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.
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
Advanced
Basic
Advanced
Deep Learning
Deep Learning
Neural Networks
ML Models
Experiment Tracking
APIs
Version Control
Containers
LLMs
Generative AI
AI & Automation
Algorithms & Models
Neural Networks
Generative Models
Deployment & CI/CD
Risk & Stochastic Systems
Climate & Ocean Modeling
Supercomputing & Shell
Deployment
Responsibilities:
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