Biomedical AI Engineering
Research & ML EngineerBiomedical AI & time-series ML
I build ML systems for physiological time series, from longitudinal risk modelling to efficient inference on embedded hardware.
PhD candidate · Inria / Université Côte d’Azur · 1M+ BioSPPy downloads · NeurIPS 2026 paper accepted
Open to Research Engineer, Applied Scientist & ML Engineer roles
Healthcare · Biosignals · Medical devices
Selected Work01Projects
Selected projects in cardiac monitoring, efficient deep learning, and open-source biosignal processing.
Developed compact ECG models and their evaluation; worked with Caroline Stehlé on INT8 quantization and STM32 implementation.
Designed and implemented SurviFormer end to end: longitudinal ECG survival modelling, training and external validation.
Built BioSPPy’s HRV module and EDF reader; maintain its APIs, documentation and plotting system.
PythonSignal ProcessingBuilt an ECG processing pipeline and LSTM models for driver drowsiness detection in collaboration with CardioID.
02Experience
Biomedical AI research, teaching and industry collaboration.
Inria, Epione Team – Université Côte d'Azur
Designed and implemented SurviFormer end to end for longitudinal AFib risk prediction. Developed compact ECG models with Inn’Pulse, with Caroline Stehlé’s help on quantization and STM32 implementation. Research in Inria’s Epione team, supervised by Maxime Sermesant.
Université Côte d'Azur
AI & Health to Medical Professionals, Analog Electronics (BSc). Theoretical and practical sessions.
Instituto de Telecomunicações
Industry-applied collaboration with CardioID Technologies for well-being and cardiac arrhythmia detection.
Dec 2023 – Present
Université Côte d’Azur · Inria
In progress · Deep learning for cardiac monitoring.
2020 – 2022
EIT Health & Université Grenoble Alpes
European master's programme focused on healthcare innovation, entrepreneurship, and technology management.
2019 – 2021
Instituto Superior Técnico, Universidade de Lisboa
Thesis on AI for atrial fibrillation detection in single-lead invisible ECG.
03Skills
Tools and methods I use to build biomedical ML systems.
PyTorchPythonModel OptimizationData Parallelism
W&B
Docker
LinuxEmbedded Inference
STM32INT8 QuantizationI’ve worked with machine learning researchers, clinicians, biomedical engineers and industry partners.
04Publications
Published and accepted work in biomedical signal processing, cardiac AI and efficient deep learning.
NeurIPS 2026 / conference
Accepted · Publication forthcoming
FIMH 2025 / conference
Best Paper Award
SoftwareX / journal
Computing in Cardiology 2024 / conference
Best Poster Award
05Awards
Healthcare · Biosignals · Medical devices
Hiring for research engineering, applied ML or physiological signal processing? I’d be glad to discuss how my work could contribute to your team.
Research Engineer · Applied Scientist · ML Engineer — Healthcare, biosignals & medical devices
Let’s talk
