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Paid Internship
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21Open Positions

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Junior Bioinformatician Intern - Tertiary Analysis

SOPHiA GENETICS
Found 1 month ago
Location
Rolle, Switzerland
Duration (Months)
6 Months
Time
Full-time
Work Mode
On-site
Salary
Not disclosed
Visa Help
Not disclosed
Last Verified
1 month ago

Education

  • Bachelor

Skills & Qualifications

Technical Skills

  • Python
  • R
  • AI algorithms
  • variant prioritization
  • variant interpretation
  • bioinformatics pipelines
  • computational methods
  • machine learning
  • statistical inference methods
  • bioinformatics methods
  • clinical genetics
  • Genetics
  • Genomics
  • Bioinformatics
  • Computational Biology

Soft Skills

  • Full Proficiency in English
  • Working within an international and interdisciplinary team.
  • Working in a transversal team collaborating across multiple teams and departments.
  • Relentless Curious
  • Resilient & Nimble
  • Fearlessly Adventurous
  • We Decide
  • We Do
  • We Collaborate
  • We Innovate
  • We Empower
  • We Adapt
  • We Learn

Job Description

Reporting to the Head of Tertiary Analysis Research team, you will explore how state-of-the-art AI algorithms can enable faster, easier and more accurate variant prioritization and interpretation during the tertiary analysis step of the genetic testing workflow. Variant prioritization and interpretation steps are at the interface between bioinformatics pipelines and clinical interpretation of the patient data – this creates a unique and exciting opportunity to bridge cutting-edge computational methods with real-world clinical impact. You will be involved in: * Learning and understanding main principles and current challenges in tertiary analysis. * Evaluating use of different AI models to address those challenges. * Creating a prototype workflow that aims to address a selected task in tertiary analysis workflow. * Benchmarking, evaluating and reporting the analytical and technical performance of the prototype.

Requirements

  • Understanding of machine learning and statistical inference methods.
  • Basic knowledge of bioinformatics methods used in the field of genetics.
  • Understanding of concepts and mechanisms of clinical genetics.
  • Competence with programming languages (Python or R).
  • Degree in Bioinformatics, Computational Biology, Genetics, Genomics.
  • Full Proficiency in English

Related Field

  • AI & Machine Learning

Related Subfield

  • Applied Machine Learning

Languages

  • English

Nice to Haves

  • State-of-the-art tools and statistical algorithms for variant interpretation at both research- and industry-level of use.
  • Reproducible benchmarking.
  • The process of research and development in the industry setting.
  • General concepts of design controls for software as medical device development.
▶Apply Now

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