TN Visa Machine Learning Research Jobs
Machine Learning Research roles qualify for TN visa sponsorship under the USMCA's Computer Systems Analyst and Mathematician categories, depending on how the position is defined. Canadian professionals enter without a cap or lottery. Mexican nationals apply through a U.S. consulate. A relevant bachelor's degree and a U.S. employer's job offer letter are required.
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Get Access To All JobsINTRODUCTION
The Chu Lab — Department of Genome Sciences, University of Virginia School of Medicine
The Chu Lab in the Department of Genome Sciences at the University of Virginia (UVA) School of Medicine is seeking to fill Postdoctoral Researcher positions in computational biology and machine learning. The lab develops modern machine learning, generative modeling, and statistical learning frameworks to decipher single-cell and spatial transcriptomics data, with the goal of uncovering cellular and tissue dynamics underlying cancer, inflammation, and tissue senescence.
ROLE AND RESPONSIBILITIES
Successful candidates will lead one or more of the following ongoing projects:
- Developing neural differential equation and continuous-time dynamical models for spatial and single-cell transcriptomics to dissect cell–cell interactions and perturbation responses in complex tissue microenvironments.
- Building generative models of single-cell and spatial data to characterize cellular and tissue heterogeneity in cancer, inflammation, and tissue senescence.
- Developing next-generation deep-learning and statistical deconvolution methods for inferring gene regulation from bulk, single-cell, and spatial-omics data.
Candidates are also encouraged to develop independent research directions aligned with the lab's interests.
About the PI
The lab is led by Dr. Tinyi Chu, who joined UVA as Assistant Professor in 2026. Dr. Chu received his Ph.D. in Computational Biology from Cornell University and subsequently completed postdoctoral training at Memorial Sloan Kettering Cancer Center and Yale University. His work has appeared as first- or co-first-author publications in Nature Cancer, Nature Genetics, and Cell Stem Cell, spanning statistical method development, cancer transcriptional regulation, and spatial transcriptomics. He is the lead developer of widely used open-source software including BayesPrism, a Bayesian deconvolution framework selected as a Nature Cancer 2022 highlight. Dr. Chu's research has been recognized by a Damon Runyon Quantitative Biology Fellowship and is currently supported by an NIH K99/R00 Pathway to Independence Award (NHGRI) and substantial UVA institutional startup funding — providing a strongly resourced environment for ambitious, long-horizon methodological research.
MENTORSHIP AND CAREER DEVELOPMENT
The Chu Lab is built on the philosophy of "Mentorship as Collaboration," where trainees are valued as scientific collaborators rather than assistants. As a postdoctoral scientist in a newly established lab, you will receive individualized mentorship tailored to your career goals, defined by genuine intellectual exchange, direct technical engagement in algorithm and model development, and shared co-ownership of the science.
- Active Collaboration. The PI maintains an open-door policy, meets regularly with trainees, and is deeply involved to support their algorithm and model development.
- Scientific Independence. You will be supported to develop and lead your own research ideas with the freedom and computational resources required to pursue them.
- Grant Writing and Career Transition. Leveraging the PI's recent successful K99/R00 transition, you will receive step-by-step training in scientific writing, proposal preparation, and fellowship applications. Postdocs are supported and encouraged to apply for independent fellowships.
- Visibility. Full support for presenting at top-tier venues spanning machine learning and computational biology, and active assistance in building your professional network across academia and industry.
ENVIRONMENT
The Chu Lab is part of a vibrant interdisciplinary research community at UVA, with active collaborations across the UVA School of Medicine. The lab has full access to UVA's high-performance computing resources and core facilities supporting genomics and imaging.
Charlottesville, Virginia is a highly livable university town nestled at the foothills of the Blue Ridge Mountains, known for its excellent quality of life, affordability relative to other U.S. research hubs, and rich cultural and outdoor offerings.
MINIMUM QUALIFICATIONS
Ph.D. (or equivalent) in Computer Science, Applied Mathematics, Statistics, Computational Biology, Biophysics, Engineering, or a related quantitative discipline, in hand by the appointment start date.
PREFERRED QUALIFICATIONS
- Strong foundational knowledge in mathematics and statistics
- Proficiency in PyTorch (or equivalent deep-learning frameworks)
- At least one peer-reviewed publication in the previous area of research (not necessarily biology-related)
- Genuine intellectual curiosity for solving biological problems through quantitative approaches
- Prior experience with spatial transcriptomics, single-cell omics, or related biological datasets is a plus but not required — candidates from purely computational backgrounds are strongly encouraged to apply; domain-specific biological knowledge can be acquired on the job
This is a 12-month appointment with the possibility of renewal contingent upon satisfactory performance and the availability of funding. Salary is commensurate with education and experience.
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 position will sponsor applicants for work visas who meet the qualifications.
Start date is available immediately; the start date is flexible.
This position will remain open until filled. The University will perform background checks on all new hires prior to employment.
TO APPLY:
Please apply through Careers at UVA, and search for R0083959.
Complete an application online with the following documents:
- CV
- Cover letter
- Contact information for 3 references.
Upload all materials into the resume submission field, multiple documents can be submitted into this one field. Alternatively, merge all documents into one PDF for submission. Applications that do not contain all required documents will not receive full consideration.
Internal applicants: Search and apply for jobs on the UVA Internal Careers website.
For questions about the application process, please contact Bill Crane, Academic Recruiter at Xer5ff@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.
INTRODUCTION
The Chu Lab — Department of Genome Sciences, University of Virginia School of Medicine
The Chu Lab in the Department of Genome Sciences at the University of Virginia (UVA) School of Medicine is seeking to fill Postdoctoral Researcher positions in computational biology and machine learning. The lab develops modern machine learning, generative modeling, and statistical learning frameworks to decipher single-cell and spatial transcriptomics data, with the goal of uncovering cellular and tissue dynamics underlying cancer, inflammation, and tissue senescence.
ROLE AND RESPONSIBILITIES
Successful candidates will lead one or more of the following ongoing projects:
- Developing neural differential equation and continuous-time dynamical models for spatial and single-cell transcriptomics to dissect cell–cell interactions and perturbation responses in complex tissue microenvironments.
- Building generative models of single-cell and spatial data to characterize cellular and tissue heterogeneity in cancer, inflammation, and tissue senescence.
- Developing next-generation deep-learning and statistical deconvolution methods for inferring gene regulation from bulk, single-cell, and spatial-omics data.
Candidates are also encouraged to develop independent research directions aligned with the lab's interests.
About the PI
The lab is led by Dr. Tinyi Chu, who joined UVA as Assistant Professor in 2026. Dr. Chu received his Ph.D. in Computational Biology from Cornell University and subsequently completed postdoctoral training at Memorial Sloan Kettering Cancer Center and Yale University. His work has appeared as first- or co-first-author publications in Nature Cancer, Nature Genetics, and Cell Stem Cell, spanning statistical method development, cancer transcriptional regulation, and spatial transcriptomics. He is the lead developer of widely used open-source software including BayesPrism, a Bayesian deconvolution framework selected as a Nature Cancer 2022 highlight. Dr. Chu's research has been recognized by a Damon Runyon Quantitative Biology Fellowship and is currently supported by an NIH K99/R00 Pathway to Independence Award (NHGRI) and substantial UVA institutional startup funding — providing a strongly resourced environment for ambitious, long-horizon methodological research.
MENTORSHIP AND CAREER DEVELOPMENT
The Chu Lab is built on the philosophy of "Mentorship as Collaboration," where trainees are valued as scientific collaborators rather than assistants. As a postdoctoral scientist in a newly established lab, you will receive individualized mentorship tailored to your career goals, defined by genuine intellectual exchange, direct technical engagement in algorithm and model development, and shared co-ownership of the science.
- Active Collaboration. The PI maintains an open-door policy, meets regularly with trainees, and is deeply involved to support their algorithm and model development.
- Scientific Independence. You will be supported to develop and lead your own research ideas with the freedom and computational resources required to pursue them.
- Grant Writing and Career Transition. Leveraging the PI's recent successful K99/R00 transition, you will receive step-by-step training in scientific writing, proposal preparation, and fellowship applications. Postdocs are supported and encouraged to apply for independent fellowships.
- Visibility. Full support for presenting at top-tier venues spanning machine learning and computational biology, and active assistance in building your professional network across academia and industry.
ENVIRONMENT
The Chu Lab is part of a vibrant interdisciplinary research community at UVA, with active collaborations across the UVA School of Medicine. The lab has full access to UVA's high-performance computing resources and core facilities supporting genomics and imaging.
Charlottesville, Virginia is a highly livable university town nestled at the foothills of the Blue Ridge Mountains, known for its excellent quality of life, affordability relative to other U.S. research hubs, and rich cultural and outdoor offerings.
MINIMUM QUALIFICATIONS
Ph.D. (or equivalent) in Computer Science, Applied Mathematics, Statistics, Computational Biology, Biophysics, Engineering, or a related quantitative discipline, in hand by the appointment start date.
PREFERRED QUALIFICATIONS
- Strong foundational knowledge in mathematics and statistics
- Proficiency in PyTorch (or equivalent deep-learning frameworks)
- At least one peer-reviewed publication in the previous area of research (not necessarily biology-related)
- Genuine intellectual curiosity for solving biological problems through quantitative approaches
- Prior experience with spatial transcriptomics, single-cell omics, or related biological datasets is a plus but not required — candidates from purely computational backgrounds are strongly encouraged to apply; domain-specific biological knowledge can be acquired on the job
This is a 12-month appointment with the possibility of renewal contingent upon satisfactory performance and the availability of funding. Salary is commensurate with education and experience.
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 position will sponsor applicants for work visas who meet the qualifications.
Start date is available immediately; the start date is flexible.
This position will remain open until filled. The University will perform background checks on all new hires prior to employment.
TO APPLY:
Please apply through Careers at UVA, and search for R0083959.
Complete an application online with the following documents:
- CV
- Cover letter
- Contact information for 3 references.
Upload all materials into the resume submission field, multiple documents can be submitted into this one field. Alternatively, merge all documents into one PDF for submission. Applications that do not contain all required documents will not receive full consideration.
Internal applicants: Search and apply for jobs on the UVA Internal Careers website.
For questions about the application process, please contact Bill Crane, Academic Recruiter at Xer5ff@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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Get Access To All JobsTips for Finding TN Visa Sponsorship in Machine Learning Research
Frame your degree for TN eligibility
TN visa officers assess whether your field of study directly supports the role. A degree in computer science, statistics, or applied mathematics maps cleanly to ML Research. A general engineering degree may require a credential evaluation letter to establish the connection.
Target employers experienced with visa sponsorship
Research labs, AI-focused firms, and university spinouts have experience sponsoring work visas for specialized roles. Prioritize roles where the job description specifies a quantitative or computer science degree requirement, since that language directly supports TN eligibility. For Canadians, the employer will provide a support letter you present at the U.S. port of entry; for Mexicans, you'll apply through a U.S. consulate with the employer's support letter.
Search for verified sponsorship opportunities on Migrate Mate
Use Migrate Mate to filter ML Research openings by TN visa sponsorship. It surfaces roles from employers already familiar with the TN process, saving you time spent on positions where sponsorship is uncertain or unavailable.
Clarify your SOC code before the offer letter
Ask your employer to confirm whether the role will be classified under Computer Systems Analyst or a related category. The SOC code shapes the job offer letter language, and a mismatch between the letter and your actual duties is a common TN denial trigger at the port of entry or consulate.
Get your Canadian credentials formatted correctly
Canadian professionals entering by land port need their TN application package ready on arrival. Include your degree transcripts, the employer's offer letter on company letterhead, and a one-page summary of your specific ML Research duties. Officers don't retrieve documents for you.
Prepare for employer unfamiliarity with TN classification
Many U.S. companies default to H-1B visa sponsorship workflows and don't realize TN requires no petition or USCIS filing for Canadians. Come to offer negotiations ready to explain that TN processing is handled at the port of entry or consulate, not through USCIS.
Machine Learning Research TN Visa: Frequently Asked Questions
Does Machine Learning Research qualify as a TN visa occupation?
Yes, but the classification depends on how the role is defined. Most ML Research positions map to Computer Systems Analyst under the USMCA TN category, particularly when the work involves designing or analyzing computational systems. Roles with a stronger mathematical modeling focus may also qualify under Mathematician. The job offer letter must use language that aligns with the qualifying category's duties, not just the job title.
How does TN compare to H-1B for Machine Learning Research roles?
TN is faster and more predictable for Canadian professionals. There's no lottery, no annual cap, and no USCIS petition required. Canadians can be approved at a land port of entry the same day. H-1B involves a lottery with roughly a one-in-four selection rate, a multi-month USCIS process, and a premium processing fee if you need faster results. Mexican nationals still apply through a U.S. consulate, but they also avoid the H-1B lottery entirely.
What documents does my employer need to prepare for my TN application?
Your employer provides a signed offer letter on company letterhead that describes your specific ML Research duties, confirms you'll be employed in a qualifying TN occupation, states your start date and employment terms, and lists your degree requirements for the role. The letter doesn't need to be a formal USCIS filing, but it must be detailed enough for a CBP officer or consular officer to confirm TN eligibility on its face.
Where can I find Machine Learning Research jobs with TN visa sponsorship?
Migrate Mate lets you search ML Research openings filtered specifically by TN visa sponsorship, so you're not guessing which employers will support the process. Many job postings don't specify visa type, and companies that know H-1B often don't realize TN is an option. Filtering by sponsorship type upfront avoids late-stage surprises when you're already in offer discussions.
Can I switch ML Research employers while on TN status?
Yes, but you need a new TN authorization before starting work with the new employer. Canadians can obtain a new TN at a port of entry with the new employer's offer letter. Mexican nationals apply through a U.S. consulate. Unlike H-1B, there's no portability provision that lets you start immediately on a pending application, so time the transition to avoid any gap between your current TN and the new one.