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Weekend Machine Learning Postdoc Jobs in Fairfax, VA

Umd

Post-Doctoral Associate

College Park, MD

$48K - $65K/yr

The postdocs will write and publish academic papers to present research results at top venues in computer vision, computer graphics and machine learning. The postdoc may also organize and participate ...

Howard University

Assistant Professor

Washington, DC · On-site

... machine learning, and artificial intelligence. • Supervise and mentor undergraduate and graduate students, postdoctoral scholars, and junior researchers. • Collaborate with faculty across ...

SOSi

Data Scientist III

Washington, DC · On-site

This role is responsible for applying data science, machine learning, and data engineering ... May be requested to work evenings and weekends to meet program and contract needs. Working at SOSi ...

SOSi

Data Scientist III

Washington, DC

This role is responsible for applying data science, machine learning, and data engineering ... May be requested to work evenings and weekends to meet program and contract needs. Working at SOSi ...

Amazon

Applied Scientist, AWS Security

Arlington, VA

Your expertise in statistical analysis, machine learning, and big data processing will be crucial ... We don't like getting paged in the middle of the night or on the weekend, so we work to ensure that ...

New

Amazon

Applied Scientist, AWS Security

Arlington, VA · On-site

Your expertise in statistical analysis, machine learning, and big data processing will be crucial ... We don't like getting paged in the middle of the night or on the weekend, so we work to ensure that ...

New

Amazon

Data Scientist, AWS Security

Arlington, VA · On-site

As a Data Scientist on our team, you will leverage advanced analytics and machine learning to ... We don't like getting paged in the middle of the night or on the weekend, so we work to ensure that ...

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Weekend Machine Learning Postdoc information

What is a Weekend Machine Learning Postdoc?

A Weekend Machine Learning Postdoc is a postdoctoral researcher who focuses on machine learning projects and typically works on weekends or has a flexible schedule that includes weekend hours. This role often involves conducting advanced research in machine learning, developing algorithms, publishing papers, and collaborating with academic or industry teams. Weekend postdoc positions may be ideal for those balancing other commitments or seeking non-traditional work hours while continuing their research careers.

What are the typical projects and collaboration opportunities for a Weekend Machine Learning Postdoc?

As a Weekend Machine Learning Postdoc, you will often contribute to ongoing research projects, developing and refining machine learning models in collaboration with faculty, graduate students, and occasionally industry partners. While your hours are concentrated on weekends, you’ll typically participate in regular research meetings, code reviews, and may co-author papers or grant proposals. The role provides opportunities to mentor junior researchers and expand your expertise by working on interdisciplinary teams. This structure allows you to make significant research contributions while maintaining flexibility in your schedule.

What are the key skills and qualifications needed to thrive as a Weekend Machine Learning Postdoc, and why are they important?

To thrive as a Weekend Machine Learning Postdoc, you need a strong background in machine learning, statistics, and programming, typically supported by a PhD in a relevant field. Experience with tools such as Python, TensorFlow, PyTorch, and data analysis platforms, as well as familiarity with academic research methodologies, is essential. Exceptional problem-solving abilities, self-motivation, and effective communication are vital soft skills for success in research and collaboration. These skills enable you to drive innovative research, efficiently manage independent projects, and contribute meaningful insights to the field.

What is the difference between Weekend Machine Learning Postdoc vs Weekend Data Scientist?

AspectWeekend Machine Learning PostdocWeekend Data Scientist
Required CredentialsPhD in Computer Science, Machine Learning, or related fieldBachelor's or Master's in Data Science, Computer Science, or related field
Work EnvironmentAcademic research settings, universities, research labsIndustry companies, startups, consulting firms
Employer & Industry UsageResearch institutions, universities, academic grantsTech companies, finance, healthcare, retail
Common Search & ComparisonYesYes

The Weekend Machine Learning Postdoc typically involves academic research with a focus on advancing machine learning theories and models, often requiring a PhD. In contrast, a Weekend Data Scientist applies data analysis and machine learning techniques in industry settings, often with a bachelor's or master's degree. Both roles may work on similar projects but differ mainly in their environment, credentials, and end goals.

What are popular job titles related to Weekend Machine Learning Postdoc jobs in Fairfax, VA?

For Weekend Machine Learning Postdoc jobs in Fairfax, VA, the most frequently searched job titles are:

What cities near Fairfax, VA are hiring for Weekend Machine Learning Postdoc jobs?

Cities near Fairfax, VA with the most Weekend Machine Learning Postdoc job openings:

Weekend Machine Learning Postdoc jobs near you
Infographic showing various Weekend Machine Learning Postdoc job openings in Fairfax, VA as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 18% Part Time, 1% Temporary, and 2% Contract. Highlights an 85% Physical, 2% Hybrid, and 13% Remote job distribution.

Postdoctoral Fellows - Computational Biology & Machine Learning

The Henry M. Jackson Foundation for the Advancement of Military Medicine

Bethesda, MD • On-site

$52K - $71K/yr

Full-time

Posted 11 days ago

Job description

Job Summary:
The Henry M. Jackson Foundation for the Advancement of Military Medicine is a nonprofit organization dedicated to advancing military medicine. They are seeking a Postdoctoral Fellow in Computational Biology & Machine Learning to lead innovative research projects and develop AI/ML tools for cancer genomics analysis.
Responsibilities:
• Lead Innovative research. Conceive and execute computational research projects, develop novel algorithms and analytical frameworks to interrogate large-scale, multidimensional omics datasets, and translate findings into clinically meaningful insights. Motivation to lead research projects under Principal Investigator’s supervision.
• Build Artificial Intelligence (AI)/Machine Learning (ML) tools. Design, implement, document, and publicly release AI/ML models - including deep learning approaches - for integrative analysis of cancer genomic data, contributing resources that advance the broader scientific community.
• Engineer scalable pipelines. Develop and maintain robust, reproducible computational pipelines for processing, integrating, and managing complex biomedical datasets across multiple data modalities.
• Drive scientific communication. Lead and contribute to the preparation of high-impact scientific manuscripts, grant and fellowship applications, and conference presentations; represent the lab at national and international scientific meetings.
• Collaborate across disciplines. Actively contribute to team meetings and foster a culture of scientific excellence within a diverse, interdisciplinary research environment.
Qualifications:
Required:
• A PhD in Bioinformatics, Computational Biology, Systems Biology, Quantitative Genomics, Biomedical Engineering, Machine Learning, Computer Science (with a computational biology focus), or a closely related field is required.
• Candidates at all stages of their postdoctoral career (0–5 years of postdoctoral experience) are encouraged to apply.
• Strong foundation in statistical and computational modeling and data analysis applied to genomics questions is required.
• Experience with Artificial Intelligence (AI)/Machine Learning (ML) (deep learning) methods applied to cancer genomics is considered a strong asset.
• Demonstrated experience developing or applying computational or statistical pipelines to molecular, biological, clinical, or multi-omics data.
• Proficiency in Python, R, and/or C/C++, with hands-on experience using scientific computing libraries (e.g., pandas, NumPy, SciPy, scikit-learn, Bioconductor).
• Demonstrated experience building or applying computational/statistical pipelines to molecular, clinical, or multi-omics datasets.
• Proficiency with reproducible workflow management systems such as Snakemake, Nextflow, or equivalent pipeline frameworks.
• Familiarity with cloud or high-performance computing (HPC) environments, such as Google Cloud, Amazon AWS, SLURM/SGE-based clusters, or equivalent infrastructure.
• Experience applying AI/ML and deep learning methods to cancer genomics problems - particularly single-cell omics, spatial omics, epigenomics, or liquid biopsy fragmentomics is highly valued.
• Prior work with large-scale biomedical datasets, including multi-omics, single-cell, spatial, clinical genomics, or treatment-response data is highly valued.
• A track record of peer-reviewed publications commensurate with career stage in computational biology, bioinformatics, biomedical data science, or related fields is highly valued.
• Proven ability to collaborate effectively within large, interdisciplinary teams.
• Strong organizational skills with the ability to manage multiple priorities and meet deadlines in a fast-paced research environment.
• Excellent written and verbal communication skills in English, including demonstrated scientific writing ability.
• Ability to obtain and maintain a T1/Public Trust background check.
• Ability to stand or sit at a computer for prolonged periods.
Company:
The Henry M. Founded in 1983, the company is headquartered in Bethesda, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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