PhD: Multiomics and Big Data

CCAD seeks a Bioinformatics Researcher to study Alzheimer’s therapeutics using multiomics, biostatistics, Big Data and machine learning.

The successful candidate will join a CCAD research project funded by the ERANET-JPND program. The position focuses on investigating the mechanisms of action of candidate therapeutics for Alzheimer’s disease by integrating biostatistics, bioinformatics, and Big Data analytics.

Key Responsibilities

• Perform statistical analyses using the R programming language.
• Develop and apply bioinformatics workflows in Python for the analysis of transcriptomic, proteomic, single-cell transcriptomic, and interactome datasets.
• Design and implement Big Data and machine learning approaches using Python, R, and Julia.
• Integrate and interpret multi-omics datasets to identify biological mechanisms and therapeutic targets.

Additional Responsibilities

• Present and communicate research findings at project meetings and scientific discussions.
• Contribute actively to the scientific activities of the host research team.
• Participate in the team’s general activities and provide computational support for research projects.
• Collaborate on additional multi-omics projects within the laboratory as opportunities arise. Required Qualifications and Skills

Technical Knowledge

• Demonstrated proficiency in R and Python.
• Experience with Python scientific libraries, including Pandas, NumPy, PySpark, Dask, and TensorFlow.
• Familiarity with Big Data technologies such as Hive, Spark SQL, or similar distributed computing frameworks is considered an asset. Technical Skills
• Experience in the management, integration, and analysis of multi-omics datasets.

Knowledge of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and Data Management Plans (DMPs). Ability to develop, document, and maintain reproducible computational workflows and to deposit software or analytical pipelines in appropriate public repositories (e.g., GitHub, GitLab, Zenodo).

Personal Attributes

• Excellent organizational and time-management skills.
• Strong analytical thinking and attention to detail.
• Excellent communication and interpersonal skills.
• Ability to work both independently and collaboratively within a multidisciplinary research team.
• Commitment to reproducible and open science practices.