• Leadership and strategy: Project definition (why/how/what), stakeholders management, planning, retrospective, communication.

• Inter-disciplinary R&D: Embedded systems, machine learning, computational neuroscience.

• Supervised/unsupervised deep learning for spatio-temporal pattern recognition (CNNs, attention/state space models, RNNs, SNNs).

• Modeling, training and deployment of sparse neural networks on neuromorphic hardware for real-time and energy-efficient inference.

• MLOps: Config management (Hydra), training pipelines (PyTorch Lightning), experiment tracking (MLflow), SW integration (GitLab CI).

• Hardware design principles: VHDL development and FPGA prototyping (Quartus).

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