Senior Industrial AI Expert | Digital Twin & Reinforcement Learning (RL) for Process Control Specializing in Energy, Water, and Large-Scale Industrial
Aktualisiert am 17.03.2026
Profil
Freiberufler / Selbstständiger
Remote-Arbeit
Verfügbar ab: 17.03.2026
Verfügbar zu: 20%
davon vor Ort: 100%
Künstliche Intelligenz
Machine Learning
Reinforcement Learning
Data Scientist
Industrie 4.0
Internet of Things
Deep Learning
Optimierung
English
Verhandlungssicher
German
Basic (A2)
Danish
Intermediate
Azerba?ani
Muttersprache

Einsatzorte

Einsatzorte

Deutschland, Schweiz, Österreich
möglich

Projekte

Projekte

2025 ? today: Research Engineering

Role: Data Scientist (Research Engineer)
Customer: Onu Energy, Germany

Tasks:
  • Engineered high-fidelity market simulators to train Reinforcement Learning agents for automated energy procurement and trading strategies.
  • Designed and deployed autonomous RL agents (Soft Actor-Critic, PPO) for real-time decision making in volatile energy markets, reducing procurement costs.
  • Built end-to-end data pipelines (dbt, AWS Lambda) ensuring reliable data flow for critical optimization and machine learning models.
2022 ? 2024: Development of deep reinforcement learning algorithms

Role: PhD Candidate and R&D Engineer (Industrial AI)
Customer: Krüger Veolia and Aalborg University, Denmark

Tasks:
  • Pioneered AI-driven Process Control: Developed Deep Reinforcement Learning algorithms to autonomously control phosphorus removal, optimizing chemical dosage against strict environmental regulations.
  • Digital Twin Development: Built complex process simulators acting as "Gym" environments to train AI agents safely before real-world deployment (sim-to-real transfer).
  • Published multiple papers on AI-driven simulation and optimization for industrial processes.
  • Collaborated with cross-functional teams to integrate AI solutions into existing optimization modules.
2019 ? 2020: Development of digital twins for industrial plants

Role: Digital Twins Developer
Customer: Arkarah Engineering Company (Part-time), Iran

Tasks:
  • Designed digital twins for industrial units using AVEVA for automation and optimization.
  • Utilized MATLAB and Python for the modeling of units, incorporating artificial neural networks.
2017 ? 2021: Conducting socio-economic modeling

Role: Research Assistant
Customer: Sharif University of Technology (Part-time), Iran

Tasks:
  • Conducted socio-economic modeling of energy and environmental policies in British Columbia.
  • Utilized machine learning techniques to model and optimize environmental processes.
2016 ? 2020: Self-employed (Part-time)

Location: Iran
Role: Android, Arduino and Java Developer

Tasks:
  • Developed smart home applications using IoT to automate the control of electrical devices.
  • Designed and programmed Android applications using Java, SQL, and UI design.

Aus- und Weiterbildung

Aus- und Weiterbildung

2022 ? 2024
Doctor of Philosophy, in Data Science, Artificial Intelligence, Engineering Cybernetics, The Marie Sk?odowska-Curie Actions
Krüger Veolia and Aalborg University, Denmark

2016 ? 2018
Study - Engineering
Sharif University of Technology (Iran)
Degree: Master of Science

2011 ? 2015
Study - Engineering
Sahand University of Technology (Iran)
Degree: Bachelor of Science

Kompetenzen

Kompetenzen

Top-Skills

Künstliche Intelligenz Machine Learning Reinforcement Learning Data Scientist Industrie 4.0 Internet of Things Deep Learning Optimierung

Produkte / Standards / Erfahrungen / Methoden

Profile
Industrial AI Engineer & PhD in Engineering Cybernetics Specialist in Deep Reinforcement Learning, Machine Learning, and Process Control. I bridge classical engineering with modern AI to build autonomous systems for the energy and water sectors. Core Value: Sim-to-Real & Production AI.

Skills
  • Scientific AI & Control 
    • Deep Reinforcement Learning (SAC, PPO, DQN), Model Predictive Control (MPC), Digital Twins (Gym, SCADA), Optimization (Poymo, MILP), Time Series Forecasting (XGBoost, TFT, LSTM, Transformer, Informer)
  • Engineering Stack 
    • Python (PyTorch, TensorFlow) , C/C++ , MATLAB/Simulink , Docker, AWS, CUDA, MLOps (W&B, MLRun, MLflow)
  • Domain Knowledge 
    • Mathematics, Computer Science, Engineering, Process control, and Optimization
  • Soft Skills
    • Team Collaboration, Cross-functional Communication, Problem solving, Analytical Thinking, Teaching and Mentoring

Open Source & Software
  • TimeSeriesEnv (Creator) 
    • ?A custom Python library and Simulation Environment for training Control Algorithms on Time Series data (compatible with OpenAI Gym API)

Einsatzorte

Einsatzorte

Deutschland, Schweiz, Österreich
möglich

Projekte

Projekte

2025 ? today: Research Engineering

Role: Data Scientist (Research Engineer)
Customer: Onu Energy, Germany

Tasks:
  • Engineered high-fidelity market simulators to train Reinforcement Learning agents for automated energy procurement and trading strategies.
  • Designed and deployed autonomous RL agents (Soft Actor-Critic, PPO) for real-time decision making in volatile energy markets, reducing procurement costs.
  • Built end-to-end data pipelines (dbt, AWS Lambda) ensuring reliable data flow for critical optimization and machine learning models.
2022 ? 2024: Development of deep reinforcement learning algorithms

Role: PhD Candidate and R&D Engineer (Industrial AI)
Customer: Krüger Veolia and Aalborg University, Denmark

Tasks:
  • Pioneered AI-driven Process Control: Developed Deep Reinforcement Learning algorithms to autonomously control phosphorus removal, optimizing chemical dosage against strict environmental regulations.
  • Digital Twin Development: Built complex process simulators acting as "Gym" environments to train AI agents safely before real-world deployment (sim-to-real transfer).
  • Published multiple papers on AI-driven simulation and optimization for industrial processes.
  • Collaborated with cross-functional teams to integrate AI solutions into existing optimization modules.
2019 ? 2020: Development of digital twins for industrial plants

Role: Digital Twins Developer
Customer: Arkarah Engineering Company (Part-time), Iran

Tasks:
  • Designed digital twins for industrial units using AVEVA for automation and optimization.
  • Utilized MATLAB and Python for the modeling of units, incorporating artificial neural networks.
2017 ? 2021: Conducting socio-economic modeling

Role: Research Assistant
Customer: Sharif University of Technology (Part-time), Iran

Tasks:
  • Conducted socio-economic modeling of energy and environmental policies in British Columbia.
  • Utilized machine learning techniques to model and optimize environmental processes.
2016 ? 2020: Self-employed (Part-time)

Location: Iran
Role: Android, Arduino and Java Developer

Tasks:
  • Developed smart home applications using IoT to automate the control of electrical devices.
  • Designed and programmed Android applications using Java, SQL, and UI design.

Aus- und Weiterbildung

Aus- und Weiterbildung

2022 ? 2024
Doctor of Philosophy, in Data Science, Artificial Intelligence, Engineering Cybernetics, The Marie Sk?odowska-Curie Actions
Krüger Veolia and Aalborg University, Denmark

2016 ? 2018
Study - Engineering
Sharif University of Technology (Iran)
Degree: Master of Science

2011 ? 2015
Study - Engineering
Sahand University of Technology (Iran)
Degree: Bachelor of Science

Kompetenzen

Kompetenzen

Top-Skills

Künstliche Intelligenz Machine Learning Reinforcement Learning Data Scientist Industrie 4.0 Internet of Things Deep Learning Optimierung

Produkte / Standards / Erfahrungen / Methoden

Profile
Industrial AI Engineer & PhD in Engineering Cybernetics Specialist in Deep Reinforcement Learning, Machine Learning, and Process Control. I bridge classical engineering with modern AI to build autonomous systems for the energy and water sectors. Core Value: Sim-to-Real & Production AI.

Skills
  • Scientific AI & Control 
    • Deep Reinforcement Learning (SAC, PPO, DQN), Model Predictive Control (MPC), Digital Twins (Gym, SCADA), Optimization (Poymo, MILP), Time Series Forecasting (XGBoost, TFT, LSTM, Transformer, Informer)
  • Engineering Stack 
    • Python (PyTorch, TensorFlow) , C/C++ , MATLAB/Simulink , Docker, AWS, CUDA, MLOps (W&B, MLRun, MLflow)
  • Domain Knowledge 
    • Mathematics, Computer Science, Engineering, Process control, and Optimization
  • Soft Skills
    • Team Collaboration, Cross-functional Communication, Problem solving, Analytical Thinking, Teaching and Mentoring

Open Source & Software
  • TimeSeriesEnv (Creator) 
    • ?A custom Python library and Simulation Environment for training Control Algorithms on Time Series data (compatible with OpenAI Gym API)

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