Andrei Roibu, PhD

Applied AI Systems Engineer with Oxford PhD

2 minute read

I love solving problems and delivering end-to-end solutions, bridging deep technical expertise with project delivery and clear stakeholder communication.

My most rewarding work sits at the intersection of disciplines. True breakthroughs don’t happen in silos.

Career Snapshot

  • Synthara AG: I work in a customer-facing engineering setting, supporting 15+ pre-sales engagements by translating requirements into KPI-driven analysis and engineering automated benchmarking pipelines, reducing turnaround from weeks to hours. I also contribute to team execution through lightweight planning (Lean Agile/Kanban), driving the applications roadmap, and mentoring colleagues.
Presenting on In-Memory Computing at Embedded World.
Presenting on In-Memory Computing at Embedded World 2025.
  • University of Oxford & Roche: Developed 3D multimodal deep learning and data analysis pipelines, linking complex brain ageing processes to biological, environmental, and lifestyle factors and supporting research into neurodegenerative diseases.
  • GE Healthcare: Coordinated a small agile team partnering with clinicians to build a local AI-powered tool providing editable brain MRI segmentations of problematic neurodegenerative regions.
  • University of Sheffield: Explored the training of classical and deep reinforcement learning agents with neuromorphic hardware-compatible algorithms.
  • Rolls-Royce: Modelled regional aviation market dynamics, investigating the feasibility and total addressable market of hybrid-electric regional aircraft.
Presenting market modelling analysis.
Presenting my work at Rolls-Royce at BCUR 18.

Education & Awards

  • University of Oxford: PhD in Applied AI and Biomedical Data Science
    • Thesis: Deep Learning Approaches to Multimodal MRI Brain Age Estimation (link)
    • Awards: Royal Commission for the Exhibition of 1851 Industrial Fellowship (2021) (link), Gap Summit Leader of Tomorrow (2020)
  • University of Sheffield: MEng in Aerospace Engineering
    • Thesis: Design of Artificial Intelligence Agents for Games using Deep Reinforcement Learning (link)
    • Awards: First-Class Honours, Valedictorian, Mappin Medal and Premium (Top Graduating Student, 2019), Sir Harold West Award (2014)
Oxford PhD Graduation.
During my 2024 PhD Graduation from Oxford.

Skills and Competences

  • Programming: Python (8 years), C/C++ (familiar), Bash, Matlab, SQL (familiar), LaTeX;
  • Frameworks: PyTorch, TensorFlow, TFLite, Keras, ONNX, Scikit-Learn, Statsmodels, OpenAI Gym, NumPy, SciPy, Pandas, Matplotlib, Seaborn, PyMongo (MongoDB), h5py (HDF5), SQLAlchemy;
  • Infrastructure: Azure, Google Cloud, Docker, FastAPI, Slurm/HPC, Git CI/CD/CT, Nuitka, pytest;
  • Deep Learning: ANNs, Computer Vision (CNNs, Vision Transformers), Natural Language Processing (RNNs, LSTMs, GRUs, LLMs), Generative AI, Reinforcement Learning.
  • Data Science: Multimodal Data Fusion, Statistical Analysis, Data Engineering, HPC Pipelines, Data Visualisation
  • Embedded Programming: TinyML, In-Memory Computing, Digital Signal Processing, Optimisation, Quantisation, Emulation
  • Project Management: Agile, Lean, Kanban, Cross-Functional Communication, Product Roadmaps, Design Thinking, Change Management
  • Business Development: Technical Marketing, Horizon Scanning, Research Translation, Workshops, Pitching, Pre-Sales

My Guiding Philosophy

  1. Frame the customer and their needs.
  2. Define the jobs to be done.
  3. Use the right tools for the job.
  4. Iterate quickly, test thoroughly, and ask for regular feedback.
  5. Modularity favours scalability.