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Temporary Machine Learning Postdoc Jobs in Houston, TX

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Temporary Machine Learning Postdoc information

What skills and qualifications are needed to thrive as a temporary machine learning postdoc?

To thrive as a Temporary Machine Learning Postdoc, you need a PhD in a relevant field, a solid grasp of machine learning theory, and strong programming skills (often in Python or R). Experience with tools such as TensorFlow, PyTorch, and high-performance computing environments, as well as a record of peer-reviewed research, is typically required. Strong analytical thinking, collaboration, and effective communication help you stand out in this research-intensive role. These skills are essential for advancing cutting-edge research, publishing impactful findings, and contributing to interdisciplinary projects.

What types of projects and collaborations can a temporary machine learning postdoc expect to engage in?

A Temporary Machine Learning Postdoc typically works on cutting-edge research projects, often contributing to ongoing studies or initiating novel investigations within the field. Collaboration is common, both within their immediate research group and with interdisciplinary teams, such as data scientists, domain experts, or industry partners. Postdocs may also mentor graduate students, present findings at conferences, and publish papers, gaining valuable experience that can lead to academic or industry roles. The environment is fast-paced and research-driven, offering opportunities for professional growth and expanding one's research portfolio.

What is a temporary machine learning postdoc?

A Temporary Machine Learning Postdoc is a fixed-term research position, typically held at a university or research institution, focused on advancing knowledge and techniques in machine learning. Postdoctoral researchers in this role work on specific projects, often collaborating with faculty, graduate students, or industry partners. The position is designed to provide advanced training and research experience after earning a PhD, usually lasting from several months to a couple of years. Temporary postdocs may contribute to publishing academic papers, developing algorithms, and mentoring students, while preparing for longer-term academic or industry careers.

What is the difference between Temporary Machine Learning Postdoc vs Data Scientist?

AspectTemporary Machine Learning PostdocData Scientist
CredentialsPhD in Computer Science, Data Science, or related fieldBachelor's or Master's in Data Science, Computer Science, or related field; often requires experience
Work EnvironmentAcademic or research institutions, labsCorporate, tech companies, startups
Employer & Industry UsageUniversities, research centersBusiness, technology, finance, healthcare
Search & Comparison IntentUnderstanding research-focused roles, academic opportunitiesIndustry roles, applied data analysis, business impact

The Temporary Machine Learning Postdoc is primarily research-oriented, often in academic or research settings, requiring a PhD. In contrast, a Data Scientist typically works in industry, applying data analysis and machine learning to solve business problems, often with a Bachelor's or Master's degree. Both roles involve machine learning skills but differ in environment, focus, and experience level.

What are popular job titles related to Temporary Machine Learning Postdoc jobs in Houston, TX?

For Temporary Machine Learning Postdoc jobs in Houston, TX, the most frequently searched job titles are:

What job categories do people searching Temporary Machine Learning Postdoc jobs in Houston, TX look for?

The top searched job categories for Temporary Machine Learning Postdoc jobs in Houston, TX are:

What cities near Houston, TX are hiring for Temporary Machine Learning Postdoc jobs?

Cities near Houston, TX with the most Temporary Machine Learning Postdoc job openings:

Postdoctoral Fellow - Bioinformatics & Computational Biology

MD Anderson

Houston, TX • On-site, Remote

$64K - $76K/yr

Full-time

Medical, Dental, Retirement, PTO

Re-posted 27 days ago


MD Anderson Cancer Center rating

8.4

Company rating: 8.4 out of 10

Based on 171 frontline employees who took The Breakroom Quiz

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Job description

A full-time postdoctoral fellow position is available in Dr. Ye Zheng's lab at the Department of Bioinformatics and Computational Biology, the University of Texas MD Anderson Cancer Center. We are seeking a highly motivated and dedicated postdoctoral researcher to join our dynamic, hybrid, and highly collaborative lab.

This computational postdoctoral fellow candidate is expected to leverage single-cell/bulk-cell multi-omics, spatial omics, and pathological imaging data to reveal the cancer-specific mechanisms underlying the differential efficacies and toxicities of treatments across patients. This position offers an exciting opportunity to contribute to pioneering biological, clinically important and methodologically challenging problems by innovating cutting-edge statistical models, computational methods and AI agent skills. This position provides extensive training in grant writing, with a focus on prestigious early career development grants such as the K99 and Damon Runyon awards.

Dr. Zheng's lab works on problems at the interface of statistical, computational and biomedical sciences. The lab has developed methods to decipher gene cis-regulatory mechanisms from transcriptomics, epigenomics, proteomics and three-dimensional (3D) chromatin interaction perspectives.

All duties and responsibilities are carried out in compliance with institutional policies, ethical research standards, and applicable federal and state regulations. LEARNING OBJECTIVES The postdoctoral fellow will achieve the following learning goals: (1) develop rigorous and reproducible statistical and machine learning methods for integrating multi-modality cancer datasets, with strong benchmarking and uncertainty awareness, and deliver these methods as well documented computational tools; (2) build AI pathology models that convert tissue morphology into quantitative features to support downstream molecular interpretation, including deconvolution and harmonization approaches for robust comparison across patients, cohorts, and tissue types; (3) create agentic AI workflows that automate analysis from data ingestion and quality control to interpretation and report generation, with emphasis on transparency, auditability, and scalability on high performance computing systems; (4) conduct integrative modeling of 3D genome organization and cross platform cell surface protein measurements to improve gene regulation insight and cell type and state characterization; (5) develop professional skills through structured mentorship in manuscript writing, scientific communication, and career development applications, including K99 R00 and Damon Runyon. ELIGIBILITY REQUIREMENTS Candidates with a Ph.D

in Computer Science, Statistics, Biostatistics, Bioinformatics, Computational Biology, Engineering, Data Science, or a related field are encouraged to apply. 1. Solid training in statistics and mathematics: Past course or research training in statistics, including but not limited to mathematical statistics, statistical inference, and linear regression.

2. Strong computational skills: Proficient in developing computational tools and modern AI agent-related workflows. Proficient in programming languages R, Python, and Shell, has extensive experience in using high-performance computing environments on Linux servers, and knows how to submit batch-run jobs.

Experienced in processing and analyzing bulk/single-cell genomic data, spatial omics data, or image data. Ability to conduct highly organized and reproducible research. 3.

Genomics knowledge: Have experience working on genetic or genomic data. Can interpret the biological findings. 4.

Strong communication, writing, and collaboration ability. 5. First, co-first, corresponding, or co-corresponding publications and reprints under review on computational and/or statistical methodology development are required to demonstrate academic writing ability.

ADDITIONAL APPLICATION INFORMATION Lab website and potential research project descriptions: https://compbiowizard.github.io./ To apply, please email the following to Dr. Ye Zheng at yzheng8@mdanderson.org. (1) a cover letter describing past contributions to the field, future research plan, career development plan, scientific motivation and interests that align with Dr

Zheng's lab, (2) a curriculum vitae that includes publications and GitHub links to past project codes or developed software (3) emails and phone numbers of a list of three references POSITION INFORMATION MD Anderson offers full-time postdoc positions with a salary ranging from $64,000 to $76,000. depending on the number of years of postgraduate experience. The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition Offsite work arrangements are subject to approval and may be modified or revoked at any time based on business needs, performance considerations, or regulatory requirements.

This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment. It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law

http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html Apply


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