Description

Title: Short Error-Correcting Codes for Reliable DNA-Based Data Storage
Location: I3S Lab CNRS, 2000 Rte des Lucioles, 06900 Sophia Antipolis
Supervisors: Serge Kas Hanna (Junior Professor) and Marc Antonini (Directeur de Recherche)
Expected starting date/duration: ASAP / 3 years
Funding: The PhD student will be recruited under the SECC-DNA project, funded by the French National Research Agency (ANR) through its Young Researchers programme (JCJC).
Context: DNA has emerged as a promising molecular alternative to conventional magnetic and electronic storage media due to its exceptional density, durability, and energy efficiency. One gram of DNA can theoretically store exabytes of data! However, storing data in synthetic DNA introduces insertion, deletion, and substitution errors, while synthesis technologies limit sequences to only a few hundred nucleotides in length.
 
This PhD position is funded by the ANR JCJC SECC-DNA project, which focuses on developing short error-correcting codes for reliable and resource-efficient DNA storage. The student will design, implement, and analyze coding strategies for recovering data from noisy DNA sequencing reads, seeking to improve reliability while limiting redundancy and computational complexity. The work will also involve integrating the proposed codes into a complete DNA storage system and contributing to the preparation of experimental validation with molecular biology partners.

Responsibilities: As a PhD student, you will be involved in a comprehensive exploration of coding strategies for errors in DNA storage. Key responsibilities include:

• Literature Review: Review existing coding methods for DNA storage, with a focus on short blocklength codes for correcting insertion, deletion, and substitution errors.

• Code Design: Develop and implement improved code constructions and efficient encoding and decoding algorithms, building on existing approaches and exploring new designs.

• Theoretical Analysis: Analyze the proposed schemes in terms of code rate, encoding and decoding complexity, and decoding error probability, seeking theoretical performance guarantees.

• Experiments: Conduct numerical simulations and wet-lab experiments to validate and refine the coding strategies.