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Computational Scientist - AI/ML

Nextonic Solutions

Salary not specified
Oct 9, 2025
Severn, MD, US
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Nextonic Solutions is seeking a Computational Scientist - AI/ML for Omics Integration to develop and apply advanced AI/ML approaches to integrate multi-omics datasets for comprehensive organoid characterization and quality assessment.

Requirements

  • demonstrated experience applying AI/ML methods to biological systems.
  • Strong programming skills in Python and R are required, along with experience with machine learning frameworks and statistical analysis packages.
  • Knowledge of multi-omics data integration techniques and experience with biological pathway analysis are necessary.
  • Familiarity with cloud computing platforms and high-performance computing environments is required.
  • Previous experience working with organoid datasets or tissue engineering applications is highly desirable.
  • Experience with deep learning approaches for biological data, knowledge of systems biology principles, and familiarity with network analysis methods will be considered valuable assets.

Responsibilities

  • design and implement machine learning algorithms that integrate diverse omics datasets including genomics, transcriptomics, proteomics, and metabolomics data to create comprehensive organoid characterization profiles.
  • develop predictive models that assess organoid quality and functionality based on molecular signatures and identify biomarkers that correlate with successful organoid development.
  • creating computational tools for comparing organoid characteristics across different protocols and laboratories to support standardization efforts.
  • Collaboration with experimental teams to validate computational predictions and translate findings into actionable protocol improvements will be essential.

Other

  • Candidates must hold a PhD in computational biology, bioinformatics, computer science, or a related quantitative field
  • Experience with collaborative research projects and manuscript preparation is preferred.