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Associate Machine Learning Chemistry Jobs in Virginia

Virginia Tech

Postdoctoral Associate

Blacksburg, VA · On-site

... in AI and machine learning. The post-doctoral associate will also be responsible for data ... Chemistry or related field. - PhD must be awarded no more than four years prior to the effective ...

Carmax

Sr. Engineer, AI & ML

Richmond, VA · On-site

$120K - $180K/yr

The Senior Engineer in the Data Science and Machine Learning Engineering team at CarMax will be ... Associates based in Plano work onsite 2 days per week. Work Authorization: Applicants must be ...

CarMax, Inc.

Sr. Engineer, AI & ML

Richmond, VA · On-site

$103K - $142K/yr

The Senior Engineer in the Data Science and Machine Learning Engineering team at CarMax will be ... Associates based in Plano work onsite 2 days per week. Work Authorization: Applicants must be ...

Carmax

Sr. Engineer, AI & ML

Richmond, VA

$103K - $142K/yr

The Senior Engineer in the Data Science and Machine Learning Engineering team at CarMax will be ... Associates based in Planowork onsite2 days per week. Work Authorization: Applicants must be ...

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All JobsAssociate Machine Learning Chemistry JobsAssociate Machine Learning Chemistry Jobs in Virginia

Associate Machine Learning Chemistry information

What is the difference between Associate Machine Learning Chemistry vs Associate Data Scientist?

AspectAssociate Machine Learning ChemistryAssociate Data Scientist
Required CredentialsBachelor's or Master's in Chemistry, Data Science, or related fields; familiarity with ML frameworksBachelor's or Master's in Data Science, Statistics, Computer Science; programming skills in Python/R
Work EnvironmentResearch labs, pharmaceutical or chemical companies, biotech firmsTech companies, finance, healthcare, consulting firms
Employer & Industry UsageUsed in industries applying ML to chemical data, drug discovery, materials scienceApplied across industries analyzing large datasets, predictive modeling

Associate Machine Learning Chemistry focuses on applying machine learning techniques specifically to chemical and scientific data, often within research or pharmaceutical settings. In contrast, Associate Data Scientist has a broader scope, working with various data types across multiple industries. Both roles require strong analytical skills and familiarity with ML tools, but their industry focus and data types differ.

What are Associate Machine Learning Chemists?

Associate Machine Learning Chemists are professionals who combine expertise in chemistry with skills in machine learning to analyze chemical data, develop predictive models, and accelerate scientific discovery. They often work on tasks like predicting molecular properties, optimizing chemical reactions, and supporting drug discovery efforts using computational tools. Typically, these roles require a strong foundation in chemistry, programming experience (often in Python), and familiarity with machine learning libraries. Associate positions are generally entry-level or early-career roles, providing support to senior scientists and data scientists in research and development teams.

How does an Associate Machine Learning Chemistry professional typically collaborate with research scientists and engineers?

As an Associate Machine Learning Chemistry professional, you will frequently work alongside research scientists and chemical engineers to develop predictive models and analyze experimental data. Collaboration involves translating chemical problems into machine learning tasks, sharing insights from model results, and participating in interdisciplinary meetings to refine research objectives. Effective communication and teamwork are essential, as you may be required to explain machine learning concepts to non-technical colleagues and integrate their domain expertise into your models. This collaborative environment fosters both scientific discovery and professional growth.

What are the key skills and qualifications needed to thrive as an Associate Machine Learning Chemistry, and why are they important?

To thrive as an Associate Machine Learning Chemistry professional, you need a solid background in chemistry, data analysis, and machine learning, typically supported by a relevant degree such as chemistry, computer science, or a related field. Experience with programming languages like Python, machine learning libraries (e.g., TensorFlow, scikit-learn), and cheminformatics software is highly valued. Strong problem-solving skills, attention to detail, and the ability to communicate complex concepts clearly are crucial soft skills. These competencies enable effective collaboration on interdisciplinary teams and the development of innovative solutions in computational chemistry research.
What are the most commonly searched types of Machine Learning Chemistry jobs in Virginia? The most popular types of Machine Learning Chemistry jobs in Virginia are:
What are popular job titles related to Associate Machine Learning Chemistry jobs in Virginia? For Associate Machine Learning Chemistry jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Associate Machine Learning Chemistry jobs in Virginia look for? The top searched job categories for Associate Machine Learning Chemistry jobs in Virginia are:
What cities in Virginia are hiring for Associate Machine Learning Chemistry jobs? Cities in Virginia with the most Associate Machine Learning Chemistry job openings:
Associate Machine Learning Chemistry jobs near you
Postdoctoral Research Associate

Postdoctoral Research Associate

University of Virginia

Charlottesville, VA • On-site

Full-time

Re-posted 24 days ago

University Of Virginia rating

7.9

Company rating: 7.9 out of 10

Based on 35 frontline employees who took The Breakroom Quiz

179th of 553 rated colleges and universities

Job description

scholarship during the training period.
The Department of Genome Sciences at the University of Virginia is seeking a highly motivated Postdoctoral Research Associate to join the Miller Lab and contribute to the Leducq COMET Network, an international collaborative effort focused on understanding the mechanisms of vascular calcification and related cardiovascular diseases.
This position is an outstanding opportunity for a computational scientist with strong training in bioinformatics, machine learning, and large-scale genomic data analysis to work at the interface of human genetics, single-cell and spatial multi-omics, cardiovascular biology, and translational medicine. The successful candidate will develop and apply advanced computational approaches to identify disease-associated genes, pathways, cell states, and regulatory mechanisms involved in vascular calcification, atherosclerosis, and broader cardiovascular disease.
Founded in 1819 by Thomas Jefferson, the University of Virginia is renowned for its commitment to advancing knowledge, educating leaders, and cultivating informed citizenship. The Department of Genome Sciences addresses fundamental questions in biology, public health, and medicine by developing and applying state-of-the-art genetic, genomic, computational, and multi-omic approaches to complex human diseases. The Miller Lab focuses on unraveling cardiovascular disease mechanisms by integrating large-scale human genetics, single-cell and spatial multi-omics, functional genomics, and data science approaches.
As part of the Leducq COMET Network, the successful candidate will work in a highly collaborative international environment involving data scientists, genomicists, statisticians, vascular biologists, cardiologists, and other clinical and translational experts. The candidate will contribute to the development of scalable computational pipelines, machine learning workflows, and integrative analyses that enable mechanistic discovery across diverse genomic and multi-omic datasets.
The successful candidate will be expected to:
  • Develop and apply computational methods for the analysis of large-scale genomic, epigenomic, transcriptomic, single-cell, spatial, and multi-omic datasets relevant to vascular calcification and cardiovascular disease.
  • Build, benchmark, and maintain robust bioinformatics pipelines for data processing, quality control, integration, visualization, and reproducible analysis.
  • Use machine learning and statistical approaches to identify disease-associated genes, pathways, regulatory programs, cell states, and molecular mechanisms.
  • Integrate human genetics, functional genomics, and multi-omic datasets to prioritize candidate genes and causal pathways involved in vascular calcification and cardiovascular disease.
  • Work closely with lab members and Leducq COMET Network collaborators to harmonize datasets, refine analysis strategies, and interpret findings in a biological and clinical context.
  • Present progress in weekly group meetings and monthly consortium meetings.
  • Draft manuscripts, contribute to grant applications, and support dissemination of findings through publications and presentations at national and international conferences.
  • Contribute to the training and mentorship of junior lab members, including graduate students, undergraduate researchers, and computational trainees.

Required qualifications:
  • PhD degree in bioinformatics, computational biology, genomics, genetics, biostatistics, statistics, computer science, biomedical engineering, systems biology, or a related quantitative discipline.
  • Strong programming skills in R and Python.
  • Experience working in Linux/Unix environments and using bash, high-performance computing systems, and reproducible computational workflows.
  • Experience analyzing large-scale genomic or multi-omic datasets.
  • Familiarity with workflow management systems such as Nextflow.
  • Strong understanding of statistical analysis, data visualization, and reproducible research practices.
  • Excellent written and oral communication skills.
  • Demonstrated ability to work both independently and as part of a collaborative, cross-functional team.

Preferred qualifications:
  • Experience with single-cell RNA-seq, single-cell ATAC-seq, spatial transcriptomics, epigenomics, proteomics, or other high-dimensional omics datasets.
  • Familiarity with cardiovascular biology, vascular disease, vascular calcification, atherosclerosis, or related disease areas.
  • Experience with machine learning frameworks and workflows, including PyTorch, scikit-learn, and standard supervised and unsupervised learning approaches.
  • Experience developing, containerizing, and documenting reusable computational pipelines.
  • Familiarity with version control, package development, cloud or HPC deployment, and collaborative coding practices.
  • Prior experience contributing to manuscripts, grants, consortium projects, or large collaborative research efforts.

Ideal candidate profile:
The ideal candidate will be a rigorous and creative computational scientist who enjoys developing new analytical approaches while working closely with experimental, clinical, and quantitative collaborators. They will have a strong track record of programming, data
analysis, and problem-solving, along with the ability to communicate complex computational results clearly to both technical and non-technical audiences. A strong team-oriented mindset and enthusiasm for mentoring junior researchers are essential.
This is a restricted position, which is dependent on funding and is contingent upon funding availability. This is a 12-month appointment with the possibility of renewal contingent upon satisfactory performance and the availability of funding.
This position is based in Charlottesville, VA, and must be performed fully on-site.
Salary range : 50-70k yearly will be commensurate with education and experience
How to Apply
Please apply online, by searching for requisition number R0083987. Complete an application with the following documents:
  • CV (required)
  • Cover letter (required)
  • Academic transcripts (optional)
  • Names of 3 references (required)

Upload all materials into the resume submission field. You can submit multiple documents into this one field or combine them into one PDF. Applications without all required documents will not receive full consideration.
Internal applicants: Search and apply for jobs on the UVA Internal Careers website.
The University of Virginia is an equal opportunity employer. All interested persons are encouraged to apply, including veterans and individuals with disabilities. Learn more about UVA's commitment to non-discrimination and equal opportunity employment .

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About University of Virginia

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The University of Virginia is distinctive among institutions of higher education. Founded by Thomas Jefferson in 1819, the University sustains the ideal of developing, through education, leaders who are well-prepared to shape the future of the nation.

Industry

Colleges, universities, and professional schools

Company size

10,000+ Employees

Headquarters location

Charlottesville, VA, US

Year founded

1819

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