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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Job offer - **Company:** CNRS - **Location:** Paris, France - **Contract:** Temporary contract - **Skills:** Analysis of Variance (ANOVA), Artificial Neural Networks, Bioinformatics, Collaborative Software, Databases, Data Transformation, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, NumPy, Tensorflow, Signal Processing, Jupyter Notebook, Scripting, Pytorch, Random Forest, Deep Learning, Model Validation, Git, Pandas, Matplotlib, Scikit Learn, Feature Selection, Feature Extraction, Software Version Control, Unsupervised Learning - **Published:** August 26, 2026 - **Apply:** https://emploi.cnrs.fr/Offres/CDD/UMR8263-ISAVIV-009/Default.aspx ## About the Role PhD or equivalent Research Field Biological sciences, Mandatory Skills in Neuroscience and Modeling * Experience in developing supervised and unsupervised learning methods: o Mastery of classification algorithms (SVM, Random Forest, KNN) and regression. o Experience in feature selection and model validation. * Knowledge of deep learning methods: o Experience with CNNs (for signal analysis) and possibly other architectures (RNN, Transformers). o Ability to adapt these methods to complex biological data. * Knowledge of biological data in signal theory: o Experience with physiological signals (ECG, EEG, EMG): acquisition, preprocessing, feature extraction. o Understanding of specific challenges related to the analysis of these signals (noise, artifacts, inter-individual variability). * Knowledge of statistical processing and biological data modeling: o Mastery of statistical tests (ANOVA, regression, non-parametric tests). o Experience in predictive modeling applied to biology or medicine. Mandatory Skills in Animal Experimentation * Proven experience in handling murine models (mice/rats): o Knowledge of neuromuscular stimulation protocols. o Mastery of physiological data acquisition techniques (EMG, respiration, SpO2). * Compliance with ethical and regulatory standards related to animal experimentation. Computer Development Skills * Confirmed experience in Python: o Mastery of libraries: scikit-learn, tensorflow/pytorch, pandas, numpy, matplotlib/seaborn. o Ability to develop robust scripts for preprocessing, analysis, and modeling. * Use of collaborative tools: o Git (version control), Jupyter Notebooks, development environments (VS Code, etc.). Additional comments ## Description * Building a preclinical database using murine models (mice/rats) to study the impact of respiratory neuromuscular stimulation. * Developing predictive models (supervised/unsupervised learning, deep learning) to identify responders/non-responders and optimize stimulation programs. * Validating models through experimental protocols on animals, combining biology, signal processing, and data science. The project is structured around two main axes: 1. Animal Experimentation: Acquisition of physiological data (EMG, respiration, SpO2, etc.) from murine models. 2. Predictive Modeling: Development of advanced machine learning methods to analyze this data and propose optimal stimulation programs. Main Responsibilities: 1. Animal Experimentation (30 à 40% of time) * Design and implementation of protocols on murine models: o Measurements at D0, D2, D3, D15: weight, respiratory parameters (Vt, Fr, Ti, Te), EMG (diaphragm/intercostal/abdominal), SpO2, lean mass index, respiratory variability. o Compliance with good practices (ethics, regulations on animal experimentation). * Data acquisition and preprocessing: o Cleaning, structuring, and annotating data to make it usable for machine learning. o Automation of data collection pipelines (Python scripts). 2. Predictive Modeling (60 à 70% of time) * Development of supervised and unsupervised learning models: o Classification (SVM, Random Forest, KNN) to predict response to stimulation. o Regression to estimate the effectiveness of stimulation programs. * Application of deep learning methods: o Use of neural networks (CNN, possibly RNN/Transformers) to analyze biological signals (EMG, respiration). o Integration of multimodality (combining numerical data and raw signals). * Statistical processing and biological data modeling: o Analysis of signal variability (ECG, EEG, EMG). o Selection of relevant features and model interpretability (SHAP, LIME). * Model validation: o Performance evaluation (AUC-ROC, precision/recall, RMSE). NeAR Team, Environment: 9 Principal Investigator researchers, 5 PhD students, 3 study engineers; a specific respiratory research group composed of 3 PhD students and 1 study engineer. Shared experimental areas, teamwork highly valued, weekly working meetings, and weekly lab and/or unit seminars. ## Related Videos - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Vectorize all the things! 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