J-1 Visa Machine Learning Research Jobs
Machine Learning Research positions in the U.S. typically fall under the J-1 visa Research Scholar or Specialist category, with sponsorship issued by a State Department-designated organization rather than your host employer. Placement at universities, national labs, or AI research institutes is common, and some positions carry a two-year home residency requirement.
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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.
See all J-1 Visa Machine Learning Research Jobs
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Get Access To All JobsTips for Finding J-1 Visa Sponsorship in Machine Learning Research
Document your research output before applying
Compile publications, conference papers, and technical reports into a single portfolio. Designated J-1 sponsors evaluate your scholarly record when assessing Research Scholar category eligibility, and gaps in documentation slow DS-2019 issuance significantly.
Confirm whether your role triggers the home residency requirement
Machine Learning Research positions funded by a government agency or tied to skills on the Exchange Visitor Skills List may carry a two-year home residency requirement. Verify your situation with your prospective designated sponsor before accepting any offer.
Target host institutions with active designated sponsor agreements
Universities, national laboratories like Argonne or Oak Ridge, and private AI research institutes often hold standing agreements with sponsors like IIE or Cultural Vistas. Search Migrate Mate to filter for ML research roles at organizations already familiar with the J-1 host process.
Negotiate a training plan before your start date
Your designated sponsor must approve a written training plan before issuing the DS-2019. Have your host institution's research office draft this document early, mapping your ML research objectives to specific deliverables and supervision arrangements.
Distinguish Research Scholar from Specialist when your role is narrow
If your engagement is a defined technical consultation rather than open-ended research, your sponsor may place you in the Specialist category instead. Specialist status caps your stay at one year with no extension, so clarify program category expectations during the offer stage.
Check the wage floor against prevailing rates before signing
Even though J-1 is not employer-sponsored, your host institution must pay at least the prevailing wage for your occupation and location. Cross-reference your offered compensation against the OFLC Wage Search for your SOC code before committing.
Machine Learning Research J-1 Visa: Frequently Asked Questions
Which J-1 program category applies to Machine Learning Research roles?
Most Machine Learning Research positions fall under the Research Scholar category, which covers postdoctoral researchers, visiting scientists, and faculty engaged in original research at U.S. universities or research institutions. If your role is a defined technical engagement rather than open-ended inquiry, your designated sponsor may classify you under the Specialist category instead, which carries a one-year maximum stay.
Who actually sponsors the J-1 visa for a Machine Learning Research position?
The visa sponsor is a U.S. Department of State-designated organization, not your host employer. Entities like IIE, Cultural Vistas, or a university's own designated sponsor office issue the DS-2019 form that initiates your visa application. Your host institution, such as a university lab or AI research center, is the placement site, but it does not hold sponsorship authority itself.
How do I find U.S. employers and research institutions open to J-1 Research Scholar placements?
Use Migrate Mate to search for Machine Learning Research roles at institutions with J-1 hosting history. Universities, federally funded research labs, and some private AI institutes regularly host J-1 exchange visitors and have established relationships with designated sponsors. Filtering by institution type helps you avoid organizations unfamiliar with the host-employer compliance requirements.
Does a Machine Learning Research J-1 position trigger the two-year home residency requirement?
It depends on two factors: whether your home country appears on the Exchange Visitor Skills List for machine learning or related fields, and whether your position receives U.S. government funding. If either condition applies, you'll be subject to the two-year home residency requirement before you can change to most other U.S. visa categories. Confirm this with your designated sponsor before accepting an offer.
Can a Machine Learning Research J-1 position lead to an H-1B or other long-term work visa?
Yes, but the path depends on whether you're subject to the two-year home residency requirement. If you're not subject to it, you can apply for an H-1B visa or pursue other nonimmigrant categories after your J-1 ends. If you are subject to it, you'll need either a waiver or to fulfill the requirement first. Many ML researchers use J-1 as a bridge to a tenure-track position or industry role that then sponsors longer-term status.