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Data Annotation Research Jobs in Bryan, TX (NOW HIRING)

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Data Annotation Research information

What qualifications do I need for data annotation?

Data annotation research roles typically require basic computer skills, attention to detail, and familiarity with annotation tools or platforms. A high school diploma or equivalent is usually sufficient, though some positions may prefer experience with data labeling, machine learning concepts, or specific software. Strong communication skills and the ability to work independently are also beneficial.

What are some common challenges faced in Data Annotation Research roles, and how can they be addressed?

Professionals in Data Annotation Research often encounter challenges such as maintaining consistency in labeling, dealing with ambiguous data, and managing large datasets efficiently. These issues can be addressed by following detailed annotation guidelines, participating in regular calibration sessions with the team, and utilizing annotation tools that support quality control checks. Collaboration with data scientists and project managers is essential to clarify ambiguities and ensure that annotated data meets the project's requirements. Staying proactive in communication and continuous learning helps to minimize errors and improve overall data quality.

Does data annotation actually pay?

Data annotation research jobs typically pay hourly or per task rates, with wages ranging from minimum wage to higher rates depending on experience and complexity of the work. Many positions are freelance or remote, requiring basic skills in data labeling tools and attention to detail. Payment is generally reliable, but rates vary by employer and project.

How hard is it to get hired by data annotation?

Getting hired for a data annotation research role typically requires basic computer skills, attention to detail, and sometimes familiarity with annotation tools or platforms. Many positions are entry-level and do not require advanced education, making the hiring process relatively accessible for those with the right skills and reliability.

What is the difference between Data Annotation Research vs Data Labeling Specialist?

AspectData Annotation ResearchData Labeling Specialist
CredentialsTypically requires a background in data science, research methods, or related fieldsOften requires basic technical skills and experience with labeling tools
Work EnvironmentResearch labs, tech companies, or remote research teamsData centers, tech companies, or remote labeling teams
Industry UsageUsed in AI/ML research, developing annotation methodologiesUsed in preparing datasets for machine learning models
Search & Comparison IntentUnderstanding research-focused roles in data annotationLooking for practical data labeling jobs

Data Annotation Research involves exploring new annotation techniques and improving data quality for AI models, often requiring research skills. In contrast, Data Labeling Specialists focus on applying existing labeling tools to annotate datasets efficiently. Both roles are essential in AI development but differ in scope and expertise.

Is data annotation real or fake?

Data annotation is a real and essential process in machine learning and AI development, involving labeling data such as images, text, or audio to train algorithms. Data annotation jobs require attention to detail and often use tools like labeling platforms or software, making them a legitimate employment opportunity in the tech industry.

What is data annotation research?

Data annotation research involves studying and developing methods for labeling data, such as images, text, or audio, to be used in training machine learning models. Researchers in this field focus on improving annotation accuracy, efficiency, and scalability, as well as addressing challenges like bias and consistency. This work is critical because high-quality annotated data is essential for building effective AI systems. Data annotation research often includes exploring new tools, techniques, and guidelines for human annotators or automated labeling systems.

What are the key skills and qualifications needed to thrive as a Data Annotation Researcher, and why are they important?

To thrive as a Data Annotation Researcher, you need strong attention to detail, analytical thinking, and familiarity with data labeling concepts, often supported by a degree in computer science, linguistics, or a related field. Experience with annotation platforms, data management tools, and sometimes knowledge of programming languages like Python are typically required. Excellent communication, problem-solving abilities, and the capacity to work independently set standout contributors apart. These skills ensure high-quality, accurate data labeling, which is crucial for developing reliable AI and machine learning models.
What job categories do people searching Data Annotation Research jobs in Bryan, TX look for? The top searched job categories for Data Annotation Research jobs in Bryan, TX are:
What cities near Bryan, TX are hiring for Data Annotation Research jobs? Cities near Bryan, TX with the most Data Annotation Research job openings:
Data Annotation Research jobs near you

Postdoctoral Research Associate

Texas Agricultural Experiment Station

College Station, TX • Hybrid

$4.1K/mo

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 13 days ago

Job description

Job Title

Postdoctoral Research Associate

Agency

Texas A&M Agrilife Research

Department

Plant Pathology & Microbiology

Proposed Minimum Salary

$4,166.67 monthly

Job Location

College Station, Texas

Job Type

Staff

Job Description

AboutTexas A&M AgriLife

Texas A&M AgriLife is comprised of the following Texas A&M University System members:

  • Texas A&M AgriLife Extension Service

  • Texas A&M AgriLife Research

  • College of Agriculture and Life Sciences at Texas A&M University

  • Texas A&M Forest Service

  • Texas A&M Veterinary Medical Diagnostic Laboratory

As thenation'slargestmostcomprehensive agriculture program, Texas A&M AgriLife brings together a college and four state agencies focused on agriculture and life sciences within The Texas A&M University System.With over 5,000 employees and a presence in every county across the state, Texas A&M AgriLife is uniquely positioned to improve lives, environments and the Texas economy through education, research, extension and service.

Clickhereto learn more about howyoucan be a part of AgriLife and make a difference in the world!

PositionInformation

The Antony-Babu laboratory seeks a Postdoctoral Research Associate to lead the field pathology and pathogen genomics of a project building predictive tools for cotton soil-borne disease management. The work is supported through a cooperative agreement with USDA-ARS and is conducted in collaboration with the USDA-ARS Southern Plains Agricultural Research Center (Insect Control and Cotton Disease Research Unit).This is a field scientist's role with full ownership of the genomics that flows from it. You will take the major cotton soil-borne pathogens from field and greenhouse experimentation through isolate sequencing, hybrid genome assembly, comparative and population genomics, and diagnostic-tool development, and you will publish the resulting population-ecology and disease biology. The position works alongside a Ph.D. student across the whole project. We are looking for someone who is independent in field-based research and in molecular bioinformatics, and who sees genome data and disease ecology as one continuous line of work rather than separate specialties.

Research Focus
You will lead the field pathology and pathogen genomics: design and run field and greenhouse pathogen experiments, drive the isolate-to-assembly-to-diagnostic-assay pipeline, and lead population-ecology and disease publications built on that genomic work.

Responsibilities:

  • Design and execute field and controlled-environment pathology experiments, including inoculum-density gradient studies; direct undergraduate research interns during field-sampling campaigns.
  • Lead hybrid (Oxford Nanopore + Illumina) sequencing, assembly, and annotation of pathogen genomes; conduct comparative and population genomics to characterize spatial/temporal structure, virulence, and effector variation, and to identify diagnostic target regions.
  • Translate genomic targets into field-deployable molecular diagnostics (LAMP), with quantitative cross-validation by droplet digital and real-time PCR.
  • Contribute to the host-microbiome analyses (GWAS/mGWAS, metagenomics) and the integrative modeling led by the graduate student.
  • Apply machine-learning and AI-assisted tools in genome analysis, population and disease-ecology work, and pipeline development, with attention to reproducibility and validation of results.
  • Develop reproducible bioinformatic pipelines on Texas A&M HPRC resources; prepare data, figures, and first- and co-authored manuscripts.
  • Maintain accurate lab records in both digital and hardcopy form, and ensure up-to-date lab safety documentation.
  • Lead and co-author manuscripts in scientific journals; assistance may also be sought in drafting extension documents.
  • Mentor the graduate students and interns.
  • Collaborate with USDA-ARS scientists.

Required Qualifications:

  • Ph.D. (in hand by start date) in plant pathology, microbiology, microbial/molecular genomics, agronomy/crop science with a pathology focus, or a related field.

Preferred Qualifications:

  • Demonstrated field and/or greenhouse experimental experience in plant pathology or a closely related discipline.
  • Demonstrated bioinformatics capability: microbial/fungal genome assembly and annotation, comparative or population genomics, command-line work in a Linux/HPC environment, and scripting in at least one of Python, R, or Bash.
  • Hands-on molecular biology (DNA extraction, library preparation, PCR/qPCR).
  • Experience handling Oxford Nanopore (ONT) sequence data.
  • A record of scientific productivity appropriate to career stage and strong written and oral communication.
  • As a field-demanding position, a current driver's license is required. The laboratory works with machine-learning and AI-assisted tools as part of routine research practice. Prior formal experience is not required, but candidates are expected to use these tools in their work and to develop fluency with them on the job, with a strong emphasis on reproducibility and validation.
  • Experience with soil-borne pathogens of cotton or other row crops (fungal and nematodes).
  • Population genomics, microbiome analysis, or diagnostic assay (LAMP/qPCR/ddPCR) development.
  • Field-trial design and prior mentoring or supervisory experience.

Additional Requirements:

  • Ability to obtain a valid US driver's license.
  • Initial one-year appointment, renewable up to four years contingent on performance and funding.

Knowledge, Skills, and Abilities:

  • Aseptic microbiology: both conceptual and demonstrable technical knowledge.
  • Microbial culture of bacteria and fungi; ability to grow microorganisms in pure culture and in interaction studies, including the soil-borne pathogens central to this project.
  • A deep understanding of the microbial species concept is mandatory, and is expected to inform the pathogen population-genomics and diagnostic work.
  • Ability to collect phenotypic data from plants (healthy, infected, and infested) in field and greenhouse settings.
  • Fast learner and self-starter, able to work independently.
  • Meticulous record-keeping and a detail-oriented approach.
  • Knowledge of laboratory maintenance and equipment.
  • Ability to multi-task and to work cooperatively with others across internal and external collaborations.
  • Experience in handling ONT data is required, and hands-on experience in running the Oxford Nanopore sequencer is desirable.
  • Experience in high-throughput culturomics is desirable.
  • Experience in, or interest in, laboratory automation will be an advantage.

Equipment used to perform the essential duties of this position:

Computer - 5 to10 hours/week

PCR machine - 5 to 10 hours/week

Sequencer - 5 to 10 hours/week

Why Work at Texas A&M AgriLife?

When you choose toworkfor Texas A&M AgriLife, you become part of an organization that is an established leader in agriculture and life sciences with a wide range of capabilities to meet the needs of our statewide, national, and international constituents.

In addition, Texas A&M AgriLife offers a comprehensive benefit packageincluding the following:

  • Health, dental, vision, life and long-term disability insurancewith Texas A&M AgriLife contributing to employee health and basic life premiums

  • 12-15 days of annual paid holidays

  • Up to eight hours of paid sick leaveand at leasteight hours of paid vacation each month

  • Automatic enrollment in theTeacher Retirement System of Texas

  • Employee Wellness Initiative for Texas A&M AgriLife

ApplicantInstructions

Applications received by Texas A&MAgriLifemust either have all job application data entered or a resume attached. Failure to provide all job application data or a complete resume could result in an invalid submission and a rejected application. We encourage all applicants to upload a resume or use a LinkedIn profile to prepopulate the online application.

RequiredDocuments

CV/ Resume

Cover letter

List of references

Certifications/additional documentation

All positions are security-sensitive. Applicants are subject to a criminal history investigation, and employment is contingent upon the institution's verification of credentials and/or other information required by the institution's procedures, including the completion of the criminal history check.

Equal Opportunity/Veterans/Disability Employer.

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