STAGE Intern Machine Learning Scientist

Date de mise à jour de l’offre

AgenT :

AgenT is on a mission to empower everyone with the tools they need to detect, treat, and ultimately prevent Alzheimer's. We are pioneering the most comprehensive multiomics platform for early Alzheimer's detection through a routine blood draw. By combining deep expertise in molecular biology with advanced computational biology and machine learning techniques to recognize disease-associated patterns among billions of circulating, cell-free biomarkers, we are developing simple and accurate blood tests for early Alzheimer's detection and integrating the actionable insights into health systems to operationalize a machine learning feedback loop between care and science.

Description de la mission

In this role, you will work with a team of talented scientists to develop state of the art assays to detect Alzheimer's signals at an early stage. You will be involved in the design of experiments, analysis of the raw data and interpretation of the results to improve assay performance. You will lead analytical efforts to characterize and improve assay attributes to make them more efficient and sensitive. You will also design and perform simulation studies to evaluate the effect of assay modifications on the classification results.

You Will:
- Act as a core member of the R&D team leading the analytical aspects of new assay development.
- Support a team of assay scientists to characterize and evaluate assay performance.
- Run in silico experiments to simulate assay modifications and characterize assay attributes.
- Perform complex analysis of large multiomics data sets.
- Present project updates, data analysis, and experimental conclusions at technical meetings.

Profil recherché

- PhD or MS or equivalent research experience in a relevant, quantitative field such as computer science (AI or ML emphasis), statistics, applied math, engineering, or a related field.
- Strong knowledge of mathematical fundamentals: statistics, probability theory, linear algebra.
- Practical and theoretical understanding of fundamental models and algorithms in supervised and unsupervised learning: generalized linear models, kernel machines, decision trees, neural networks; boosting and model aggregation; clustering and mixture modeling; Bayesian inference and model selection, EM, variational inference, Gaussian processes, causal inference, Monte Carlo methods; dimensionality reduction and manifold learning.
- Proficiency in a general-purpose programming language: Python, Java, C, C++, etc
- Excellent ability to clearly communicate across disciplines and work collaboratively towards next steps in experimental iterations

Niveau de qualification requis

Bac + 4/5 et +
  • Employeur
  • Secteur d’activité de la structure
    Santé - Social - Citoyenneté - Sécurité
  • Effectif de la structure
    De 0 à 10 salariés
  • Site internet de la structure
  • Type de stage ou contrat
    Stage pour lycéens et étudiants en formation initiale
  • Date prévisionnelle de démarrage
  • Durée du stage ou contrat
    Plus de 4 mois et jusqu'à 6 mois
  • Le stage est-il rémunéré ?
  • Niveau de qualification requis

    Bac + 4/5 et +
  • Lieu du stage
    5 parvis Alan Turing
  • Accès et transports
    Subway on lines 14 and 6