Biostatistics Internship Opportunity

il y a 2 semaines


Pessac, Nouvelle-Aquitaine, France SOPHiA GENETICS Temps plein

Key Responsibilities:

As a Junior Biostatistician Intern at SOPHiA GENETICS, you will contribute to the development and validation of machine learning models for predictive analysis using multimodal data in oncology. This work will help predict treatment responses for various cancers, directly impacting clinical decision-making and patient outcomes.

Your Mission:

As an intern, you will be involved in preparing and cleaning multimodal data sets, including clinical and imaging data. You will also train and validate machine learning algorithms for predictive modelling in oncology, implement new routines to improve the accuracy of existing models, post-process and interpret results to generate actionable clinical insights, and present your findings and results to the team.

Requirements:

The Experience You Bring:

  • Currently pursuing a Master's degree in Biostatistics, Applied Statistics, Applied Mathematics, or a related field.
  • Proficiency in Biostatistics, machine learning, and Python.
  • Strong communication skills in English, both written and verbal.
  • Ability to manage priorities effectively.
  • A team player who collaborates well with others but can also work autonomously.

What You Will Learn:

  • Gain hands-on experience analysing and managing real-world multimodal data sets.
  • Specialise in machine learning techniques applied to oncology research.
  • Develop expertise in presenting and communicating predictive analysis results.
  • Collaborate with cross-functional teams in a dynamic and innovative environment.

About SOPHiA GENETICS:

We believe there is a smarter, more data-driven way to make decisions in healthcare and our AI SaaS Platform enables that. You will have direct input into our mission to radically improve the outcomes for Cancer & Rare Disease patients globally. Enable our customers to push industry boundaries, as we pioneer into newly discovered fields and combine complex, multi-modal data for the first time.



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