# Curriculum Vitae

- **Name**: Onur Danaci
- **Email**: onur.danaci.ai@gmail.com
- **Location**: The Hague, Netherlands
- **Fields**: Artificial Intelligence, Quantum Technologies, HPC, Control & Estimation Theory, Quantum Optics, Machine Learning
- [Google Scholar](https://scholar.google.com/citations?user=a7nzX0sAAAAJ&hl=en&authuser=1&oi=ao)
- [GitHub](https://github.com/onurdanaci)
- [LinkedIn](https://www.linkedin.com/in/onur-danaci-00495915b/)

**Overview**
Computational physicist and quantum-AI researcher with expertise bridging quantum technologies and machine learning.
Skilled in high-performance simulation, quantum hardware control, and hybrid quantum-classical algorithm design. 
Quick learner, systems thinker, and cross-disciplinary communicator with strong scientific integrity and a deep curiosity for emerging research.

## Job Experience

- **2023–2025 — Postdoctoral Researcher — Leiden University (Own Grant)**
_NWO Quantum Delta Initiative – MaZelTof-Q project_

• Developed digital twins for superconducting quantum devices using physics-aware neural
networks.
• Automated tune-up and calibration using deep RL and Bayesian optimization.
• Addressed flux crosstalk calibration via AI-driven methods.

- **2021–2023 — Postdoctoral Research Fellow — Leiden University**
_Applied Quantum Algorithms Center_

• Led projects regarding quantum advantage demonstrations on real hardware.
• Designed GPU-based quantum simulators using JAX.
• Designed and implemented a reinforcement learning-based optimization algorithm to improve
performance of variational quantum circuits under hardware-induced noise, improving con-
vergence and robustness in simulation benchmarks.
• Led projects in Hamiltonian & non-Markovian noise learning.

- **2021 — IBM-HBCU Quantum Center Research Fellow — Howard University, University of Illinois Chicago**
  
• Worked on quantum optimal control and tomography with IBM Quantum hardware.
• Developed a quantum game based on Fock state interactions in coupled waveguide systems.
• Designed a volume-law entangled quantum optical system based on the spatial geometry of
a cavity, and specific placements of qubits inside this cavity.
• Simulated the classical modes within these quantum optical cavities using FDTD methods.
Combined these classical results with full quantum modeling via Green’s dyad formalism.
• Applied surrogate modeling and RL for photonic structure optimization.

- **2014–2020 — Graduate Research Assistant — Tulane University**
  
• Designed ML and numerical methods to model the generation, propagation and control of
spatial modes of quantum light for quantum information processing in non-linear optical
systems. In particular generation multi-mode squeezed coherent states, and their propagation 
under atmospheric turbulence & other obstructions.

- **2019 — Data Science Intern — Jackson Laboratory**
  
• Optimized demultiplexing software (libraries in C++. R, Python) for single-cell omics data (mRNA-Seq) using Bayesian ML. 
Used deep convolutional neural networks to classify bioinformatic data. This internship brought me upto speed both in modern
practices of data science & machine learning engineering, and bio-statistics/bio-informatics.

- **2011–2013 — Graduate Research Assistant — Koc University**
• Modeled quantum optical and plasmonic systems using numerical tools (Qutip & Lumeruical FDTD) and Green’s dyad
formalism (to fully model quantum dynamics using classical mode solutions).

## Skills

- **Languages**: Python, C/C++, E, Bash, LaTeX, Mathematica
  
- **Frameworks**: JAX, TensorFlow, Qiskit, Cirq, Pennylane, QuTiP, Qulacs, Tensorcircuit

- **Scientific Computing**: NumPy, SciPy, CUDA, MPI, OpenMP, PetSc, Lumerical FDTD, Marlab

- **ML & Optimization**: Deep neural networks, Reinforcement Learning, Bayesian Optimiza-
tion, Surrogate Modeling

- **Tools & Platforms**: Docker, Git, SLURM, Linux (Ubuntu), Windows


## Education
- PhD, Physics – Tulane University
_Thesis_: Adaptive Feedback Control for Noise-Reduced Vacuum Squeezed States

- MS, Physics – Koç University
_Thesis_: Quantum Control of Hyperbolic Metamaterials

- BS, Physics – Middle East Technical University
_Thesis_: Tunneling of Relativistic Particles Through Magnetic Fields


## Honors & Rewards

-  **Quantum Delta Initiative Grant (NGF.1582.22.031), 2023**
MAZeLTof-Q, Machine Assisted Zero-Knowledge Tune-up of Superconducting Qubits

- **NWO SURF Compute Access Grant, 2022**
GPU Simulation of Noisy Quantum Circuits in Scale

- **Best Industrial Project, Workshop: NWO Physics for Industry, 2022**
Developed an industrially relevant method for the company Sense 360

- **Best Presenter (3rd), Photonics 2018**
Conference on photonics & Quantum Optics, Bilkent University, Ankara


## Additional Coursework

- Quantum/ML: TU Delft ML4Quantum, Stanford Statistical Learning (Lagunita), Coursera
Deep Learning Specialization

- Computational Science MSc Curriculum: Scientific Computing I–III, Optimization, Bayesian ML, Large Scale
Computation (CUDA, MPI, OpenMP), Linear Models, Data Analysis, C++