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Associate Machine Learning Chemistry Jobs in Virginia

Cmu

Associate Data Scientist

Arlington, VA

$67.90K - $68.50K/yr

Data Scientists at the SEI use advanced statistics, data analytics, machine learning, and artificial intelligence to help our government and industry clients research and solve cybersecurity ...

Virginia Tech

Postdoctoral Associate

Blacksburg, VA

... 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 ...

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Associate Machine Learning Chemistry information

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.

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 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.

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 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
Infographic showing various Associate Machine Learning Chemistry job openings in Virginia as of May 2026, with employment types broken down into 1% As Needed, 65% Full Time, 32% Part Time, and 2% Contract. Highlights an 91% Physical, and 9% Remote job distribution.
Postdoctoral Research Associate

Postdoctoral Research Associate

University of Virginia

Charlottesville, VA • On-site

Full-time

Posted 9 days ago

University Of Virginia rating

8.3

Company rating: 8.3 out of 10

Based on 33 frontline employees who took The Breakroom Quiz

92nd of 529 rated colleges and universities

Job description

We are looking for a highly motivated Postdoctoral Research Associate to join the Platig Lab at the University of Virginia. The candidate would be part of an interdisciplinary team of computational biologists, data scientists, and RNA biologists investigating the role of alternative splicing in Type 1 Diabetes (T1D). The project will leverage large-scale generation (400+ samples) of long-read RNA-seq from CD4+ T cells in a T1D cohort across multiple time points. A core aim of this project will be to develop interpretable machine learning approaches to understand how RNA binding proteins (RBPs) regulate observed splicing changes and to test putative mechanisms experimentally.
This is a unique opportunity to work at the intersection of machine learning, RNA biology, and immunology, with translational relevance to T1D.
Postdoctoral employment is temporary and is normally limited to an individual who has been awarded a Ph.D. or equivalent doctorate within the previous five years and who will be involved in full-time research or scholarship at the University. Employment as a Postdoctoral Research Associate is viewed as training and is preparatory for a full-time academic or research career, is supervised by a senior scholar, and allows the appointee to publish the results of his/her research or scholarship during the training period
This is a 12-month appointment with the possibility of renewal contingent upon satisfactory performance and the availability of funding.
Responsibilities
  • Integrate long-read and short-read RNA-seq with RBP data (motifs, eCLIP) for splicing and isoform analysis
  • Develop machine learning methods to predict functional RNA regulatory elements
  • Collaborate with wet-lab partners to design follow-up experiments
  • Publish findings and present at national conferences
  • Contribute to grant writing and mentorship of graduate students

Minimum Qualifications
  • PhD (awarded or imminent) in bioinformatics, computational biology, or a closely related field
  • Demonstrated experience using long- or short-read RNA-seq to understand alternative splicing
  • Experience using machine learning techniques in genomics
  • Strong publication record
  • Ability to communicate computational techniques to a broad audience

Preferred Qualifications
  • Experience modeling RNA binding proteins
  • Familiarity with T1D
  • Experience with machine learning interpretability approaches (xAI)

Physical Demands
This is primarily a sedentary job involving extensive use of desktop computers. The job does occasionally require traveling some distance to attend meetings, and programs.
Salary will be commensurate with education, experience, and NIH guidelines.
This is an Exempt-level, benefitted position. For more information on the benefits available to postdoctoral associates at UVA, visit postdoc.virginia.edu and hr.virginia.edu/benefits .
This position is based in Charlottesville, VA, and must be performed fully on-site.
To learn more about UVA and in the Charlottesville area, visit UVA Life and Embark CVA .
Application review/deadline This position will remain open until filled.
Background checks and pre-employment health screenings will be conducted on all new hires prior to employment.
Please apply online through Online and search for R0083627. Complete the application and upload the following required materials:
Internal applicants may search and apply for jobs on the UVA Internal Careers website .
  • Cover letter
  • Resume

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 may search and apply for jobs on the UVA Internal Careers website .
Reference checks will be completed by UVA's third-party partner, SkillSurvey, during the final phase of the interview. Five references will be requested, with at least three responses required.
For questions about the application process, please contact Bill Crane, Xer5ff@virginia.edu.
For questions about the position, please contact Jennifer Dean, jmdean@virginia.edu
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.

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10,000+ Employees

Headquarters location

Charlottesville, VA, US

Year founded

1819

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