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Freelance Bioinformatics Machine Learning Jobs in Iowa

Freelance Bioinformatics Machine Learning information

What are the key skills and qualifications needed to thrive as a Freelance Bioinformatics Machine Learning Specialist, and why are they important?

To thrive as a Freelance Bioinformatics Machine Learning Specialist, you need a strong background in biology, statistics, and programming (such as Python or R), typically supported by a relevant degree in bioinformatics, computer science, or a related field. Familiarity with bioinformatics tools (e.g., BLAST, Bioconductor), machine learning libraries (scikit-learn, TensorFlow), and experience with cloud computing platforms are highly valuable. Strong problem-solving, communication, and project management skills help distinguish top freelancers in this field. These capabilities are crucial for independently delivering accurate, actionable biological insights to clients and efficiently managing multiple projects.

What are some common challenges freelance bioinformatics machine learning professionals face when working with multiple clients?

Freelance bioinformatics machine learning professionals often encounter challenges such as managing diverse data formats, aligning project expectations, and ensuring data privacy across multiple clients. Each client may have unique datasets, varying levels of documentation, and different computational infrastructure, requiring adaptability and strong communication skills. Balancing multiple deadlines and maintaining clear, consistent reporting are also important to foster trust and long-term collaborations.

What does a Freelance Bioinformatics Machine Learning specialist do?

A Freelance Bioinformatics Machine Learning specialist applies machine learning techniques to analyze biological data, such as genomics, proteomics, and medical records, on a project-by-project basis. They typically work independently with research labs, biotech companies, or healthcare organizations to develop algorithms, build predictive models, and interpret complex biological datasets. Their work helps drive insights in areas like drug discovery, personalized medicine, and disease prediction, often leveraging tools like Python, R, and specialized bioinformatics software. As freelancers, they have the flexibility to choose projects, set their schedules, and work remotely.

What is the difference between Freelance Bioinformatics Machine Learning vs Freelance Data Scientist?

AspectFreelance Bioinformatics Machine LearningFreelance Data Scientist
CredentialsBackground in bioinformatics, biology, or related fields; knowledge of machine learningBackground in statistics, computer science, or related fields; strong programming skills
Work EnvironmentResearch labs, biotech companies, academic projects, freelance consultingVarious industries including finance, tech, healthcare, consulting
Industry UsagePrimarily biotech, healthcare, genomics, pharmaceutical sectorsBroad industry application including finance, marketing, tech, healthcare

Freelance Bioinformatics Machine Learning specialists focus on applying machine learning techniques to biological data, often working within biotech and healthcare sectors. In contrast, Freelance Data Scientists have a broader scope, working across multiple industries with diverse datasets. Both roles require strong analytical skills and programming expertise, but their industry focus and domain knowledge differ significantly.

What are popular job titles related to Freelance Bioinformatics Machine Learning jobs in Iowa? For Freelance Bioinformatics Machine Learning jobs in Iowa, the most frequently searched job titles are:
What cities in Iowa are hiring for Freelance Bioinformatics Machine Learning jobs? Cities in Iowa with the most Freelance Bioinformatics Machine Learning job openings:
Research Assistant - Anatomy and Cell Biology (Ryan Lab) 50%

Research Assistant - Anatomy and Cell Biology (Ryan Lab) 50%

University of Iowa Hospitals & Clinics

Iowa City, IA • On-site

Full-time

Medical, Dental, Life, Retirement, PTO

Posted 16 days ago


Job description

Description
The Research Assistant - Computational Scientist supports research efforts in Dr. Amy Ryan's laboratory, which studies lung regeneration and the molecular and cellular mechanisms underlying mucociliary clearance. Under general supervision the position will provide comprehensive computational and data science support across all laboratory projects, with a primary focus on advanced analysis of high-dimensional biological datasets, including bulk and single-cell RNA sequencing, epigenomics, proteomics, and multimodal data integration.
This role involves the development, implementation, and maintenance of computational pipelines; application of statistical and machine learning methodologies; and generation of integrative models of mucociliary function in lung health and disease. The Research Assistant collaborates closely with experimental scientists, trainees, and collaborators, contributing to study design, data interpretation, visualization, and dissemination. The position may also support data infrastructure efforts, including database development, data warehousing, and innovative computational tools that enhance data sharing, reproducibility, and discovery.
Position Responsibilities:
Computational and Bioinformatic Analysis
  • Perform computational analyses of biological datasets, including bulk RNA-seq and single-cell RNA-seq (scRNA-seq), following established laboratory and field-standard workflows.
  • Conduct quality control, normalization, differential expression analysis, clustering, pathway analysis, and data visualization under guidance from senior staff or investigators.
  • Apply basic to intermediate statistical and machine learning approaches to support modeling of mucociliary clearance mechanisms.

Data Integration and Computational Support
  • Assist in the integration of multimodal datasets (e.g., transcriptomic, epigenomic, proteomic, and phenotypic data).
  • Help maintain, adapt, and document computational pipelines and scripts to ensure reproducibility and accuracy.
  • Generate figures, summaries, and visualizations for manuscripts, presentations, grant applications, and internal reports.
  • Support development and maintenance of laboratory databases, data repositories, or data warehouse portals as assigned.

Research Collaboration
  • Work collaboratively with laboratory members, including faculty, staff, postdoctoral scholars, and trainees, to support data interpretation and experimental planning.
  • Communicate computational findings clearly to non-computational collaborators.
  • Assist in preparing documentation of computational methods for publications and regulatory or funding requirements.

Data Management and Compliance
  • Organize, document, and maintain computational data, code, and analysis outputs in accordance with laboratory and institutional standards.
  • Adhere to data management, security, and responsible conduct of research policies.
  • Maintain version control and analysis records to support transparency and reproducibility.

Professional Development and Laboratory Support
  • Learn and apply new computational and bioinformatic methods relevant to ongoing laboratory projects.
  • Participate in laboratory meetings and training activities.
  • Perform other research-related duties as assigned, consistent with the PRK1 classification.

Percent of Time: 50%
Staff Type: Professional & Scientific
Type of Position: Specified Term. Initial appointment is for one year. Appointment may be extended based on performance and availability of funding.
Pay Grade: 3A - https://hr.uiowa.edu/pay/plans
Benefits Highlights
  • Regular salaried position located in Iowa City, Iowa
  • Fringe benefit package including paid vacation; sick leave; health, dental, life and disability insurance options; and generous employer contributions into retirement plans
  • For more information about Why Iowa?, click here

Qualifications
Required Qualifications:
  • Bachelor's degree in computational biology, Bioinformatics, Data Science, Biostatistics, Computer Science, Biomedical Engineering, or a closely related quantitative or life-science field, or equivalent combination of education and progressively responsible experience in a research laboratory environment.
  • Experience performing computational analysis of high-throughput biological data, such as bulk RNA sequencing and/or single-cell RNA sequencing (scRNA-seq).
  • Proficiency in at least one programming language commonly used in computational biology (e.g., R and/or Python).
  • Working knowledge of standard bioinformatic workflows, including data quality control, normalization, statistical analysis, and data visualization.
  • Familiarity with at least one additional omics data type (e.g., epigenetic data such as ATAC-seq, proteomics, or similar large-scale datasets).
  • Experience working with large, complex datasets and organizing analysis outputs in a structured and reproducible manner.
  • Ability to follow established computational pipelines and analytical protocols under general supervision.
  • Strong attention to detail and ability to document computational methods, code, and results.
  • Effective written and verbal communication skills, including the ability to explain computational results to collaborators with varied scientific backgrounds.
  • Ability to work collaboratively as part of a multidisciplinary research team and manage multiple projects simultaneously.

Desirable Qualifications:
  • Experience with integration of multimodal datasets (e.g., transcriptomic, epigenomic, proteomic, and phenotypic data).
  • Familiarity with single-cell analysis frameworks and tools (e.g., Seurat, Monocle, or similar).
  • Exposure to epigenomic analysis workflows, including chromatin accessibility or regulatory element analysis.
  • Basic experience applying machine learning or predictive modeling approaches to biological data.
  • Experience developing or contributing to reproducible workflows using version control systems (e.g., Git).
  • Experience generating publication-quality figures and contributing to manuscripts, abstracts, or grant applications.
  • Interest in lung biology, regenerative medicine, mucociliary clearance, or related biomedical research areas.
  • Willingness to learn new computational methods and emerging approaches relevant to laboratory research.
  • Master's degree in computational biology, Bioinformatics, Data Science, Biostatistics, Computer Science, Biomedical Engineering, or a closely related quantitative or life-science field.

Application Process: In order to be considered, applicants must upload a CV or resume, and cover letter (under submission relevant materials) that clearly address how they meet the listed required and desired qualifications of this position.
Job openings are posted for a minimum of 7 calendar days and may be removed from posting and filled any time after the original posting period has ended. Successful candidates will be subject to a credential and criminal background check. This position is not eligible for University sponsorship for employment authorization. Up to 5 professional references will be requested at a later step in the recruitment process.
For additional questions, please contact Anne Phillips at anne-phillips@uiowa.edu.