Computational Scientist - AI/ML at Nextonic Solutions LLC

, Maryland, United States -

Full Time

Start Date

Immediate

Expiry Date

07 Jan, 26

Salary

0.0

Posted On

09 Oct, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Computational Biology, Bioinformatics, Computer Science, AI/ML Methods, Python, R, Machine Learning Frameworks, Statistical Analysis, Multi-Omics Data Integration, Biological Pathway Analysis, Cloud Computing, High-Performance Computing

Industry

IT Services and IT Consulting

Description

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. This position located in Frederick, MD at the Standardized Organoid Model Center will focus on creating computational frameworks that can assess organoid fidelity, predict functional outcomes, and identify optimal culture conditions through sophisticated data integration strategies. Overview: The Standardized Organoid Model Center is an NIH-funded initiative dedicated to advancing organoid research through the development of validated, reproducible, and well-characterized organoid models. The center brings together interdisciplinary teams of researchers to establish standardized protocols, develop quality control measures, and create resources that will benefit the broader organoid research community. Responsibilities: The successful candidate will design and implement machine learning algorithms that integrate diverse omics datasets including genomics, transcriptomics, proteomics, and metabolomics data to create comprehensive organoid characterization profiles. They will develop predictive models that assess organoid quality and functionality based on molecular signatures and identify biomarkers that correlate with successful organoid development. The role involves 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. Required Qualifications: Candidates must hold a PhD in computational biology, bioinformatics, computer science, or a related quantitative field with 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. Preferred Qualifications: 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. Experience with collaborative research projects and manuscript preparation is preferred.

Responsibilities

The successful candidate will design and implement machine learning algorithms to integrate diverse omics datasets for organoid characterization. They will also develop predictive models to assess organoid quality and functionality based on molecular signatures.