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Chemistry Post-Doctoral Associate - Unsupervised and Generative Machine Learning

United StatesUnited States·Gainesvillemid
Machine LearningData & AI
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Requirements Summary

PhD in Chemistry or related area Strong coding (Python) and algorithm design skills Expertise on ML tools for chemistry, in particular, generative AI Experience with Python, ML,

Technical Tools
Machine LearningData & AI
Classification Title:

Post-Doctoral Associate in unsupervised and generative ML for chemistry 

Classification Minimum Requirements:

PhD in Chemistry or related area

Strong coding (Python) and algorithm design skills

Expertise on ML tools for chemistry, in particular, generative AI

Experience with Python, ML, and AI for chemical applications

Job Description:

A Post-doctoral Associate in Theoretical Chemistry is available for work on a project in unsupervised and generative ML for chemical applications led by Dr. Ramon Miranda Quintana in the Department of Chemistry. We have a position available in our group to work on the development, implementation, and application of hyper-efficient unsupervised learning techniques to chemical problems, with emphasis on improvements to representation learning and generative methods. 

Must have PhD in Chemistry or related area. This position will be initially awarded for one year, and, contingent upon strong performance and conduct and availability of funds, may be renewed for up to two years. 

Expected Salary:

The salary is competitive and commensurate with qualifications and experience, and the compensation includes a full benefits package. To see more, visit benefits.hr.ufl.edu. 

Required Qualifications:

PhD in Chemistry or related area

Strong coding (Python) and algorithm design skills

Expertise on ML tools for chemistry, in particular, generative AI

Experience with Python, ML, and AI for chemical applications

Preferred:

Familiarity with HPC systems

Proven track record of research in ML/AI for chemistry

Strong coding foundation (Python)

Knowledge of C++ and CUDA
Special Instructions to Applicants:

For full consideration, applications must be submitted online. Click on Apply Now at the top of this posting. 

A complete application includes (1) a letter of application summarizing the applicant's qualifications, interests, and suitability for the position, (2) a complete curriculum vitae, (3) a list of at least two references. After initial review, letters of recommendation will be requested from the references for selected applicants. 

Applications will be reviewed beginning immediately, and the position will remain open until filled. Only complete applications will be reviewed at this time. Applications received after this date may be considered at the discretion of the committee and/or hiring authority. 

All candidates for employment are subject to a pre-employment screening which includes a review of criminal records, reference checks, and verification of education. 

The selected candidate will be required to provide an official transcript to the hiring department upon hire. A transcript will not be considered “official” if a designation of “Issued to Student” is visible. Degrees earned from an educational institution outside of the United States require evaluation by a professional credentialing service provider approved by the National Association of Credential Evaluation Services (NACES), which can be found at http://www.naces.org/. 

This requisition has been reposted. Previous applicants are still under consideration and need not reapply. 

Health Assessment Required: No

 

Advertised: Eastern Daylight Time
Applications close: Eastern Daylight Time

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Location & Eligibility

Where is the job
Gainesville, United States
On-site at the office
Who can apply
US

About this job

Who can apply
United States
Posted
Today
Employer
has 746 open jobs on Jobera in 4 countries.

Listing Details

First seen
October 15, 2026
Last seen
October 15, 2026

Posting Health

Days active
0
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0
Trust Level
51%
Scored at
October 15, 2026

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Chemistry Post-Doctoral Associate - Unsupervised and Generative Machine Learning