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Internship German Machine Learning Jobs in Bryan, TX

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Internship German Machine Learning information

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$23.5K

$39.3K

$81.1K

How much do internship german machine learning jobs pay per year?

As of Jul 29, 2026, the average yearly pay for internship german machine learning in Bryan, TX is $39,266.00, according to ZipRecruiter salary data. Most workers in this role earn between $30,000.00 and $42,400.00 per year, depending on experience, location, and employer.

Which 3 jobs will survive AI?

Jobs that require complex human skills such as machine learning engineers, data scientists, and cybersecurity specialists are likely to persist as AI automates routine tasks. These roles demand critical thinking, creativity, and specialized knowledge that are difficult for AI to replicate fully. Continuous learning and expertise in AI tools can enhance job security in these fields.

What is an Internship German Machine Learning?

An Internship German Machine Learning is a temporary training position typically offered by companies or research institutions in Germany, focusing on practical experience in machine learning. Interns work on real-world projects involving data analysis, algorithm development, and model implementation under supervision. These internships help students or recent graduates gain hands-on skills, industry exposure, and networking opportunities in the rapidly growing field of artificial intelligence and machine learning.

What is the difference between Internship German Machine Learning vs Data Scientist German?

AspectInternship German Machine LearningData Scientist German
Required CredentialsBasic programming, coursework in MLAdvanced degree in data science, statistics, or related
Work EnvironmentInternship setting, learning-focusedFull-time, project-driven
Industry UsageEntry-level roles, training programsProfessional roles, decision-making

Internship German Machine Learning positions are typically entry-level, focusing on learning and skill development, often requiring basic programming and coursework. Data Scientist German roles are more advanced, requiring higher education and experience, with responsibilities in analyzing data and building models. The internship provides a stepping stone into the data science field, while the data scientist role involves applying expertise to solve complex problems.

Can foreigners intern in Germany?

Foreigners can intern in Germany if they meet visa and work authorization requirements, which vary depending on their nationality and the internship duration. Internships often require a valid visa or residence permit, especially for non-EU/EEA citizens, and may need to comply with labor laws and internship regulations. It is important to check specific visa conditions and employer sponsorship options before applying.

How much do ML interns get paid?

Machine Learning interns typically earn between $15 and $30 per hour, depending on the company, location, and level of experience. Paid internships often include opportunities to work with tools like Python, TensorFlow, or PyTorch and may be full-time or part-time during the summer or semester.

What types of projects can I expect to work on during a German Machine Learning internship?

As a German Machine Learning intern, you'll typically assist with real-world projects such as developing and testing machine learning models, preprocessing datasets, and supporting the implementation of AI solutions in both German and international contexts. You may also help with data analysis, model evaluation, and documentation, often collaborating with data scientists and engineers. These projects provide hands-on experience with industry-standard tools and workflows, helping you build practical skills and a strong professional network.

What are the key skills and qualifications needed to thrive as an Internship German Machine Learning, and why are they important?

To thrive as an Internship German Machine Learning, you need a solid understanding of machine learning concepts, programming skills in Python, and progress toward a degree in computer science or a related field. Familiarity with tools such as TensorFlow, PyTorch, and data analysis libraries, as well as experience using version control systems like Git, is typically required. Strong analytical thinking, problem-solving ability, and effective communication—especially in both English and German—help you collaborate within diverse teams. These skills and qualifications are essential for successfully contributing to machine learning projects and adapting to the fast-evolving tech industry.

Is AI in demand?

AI skills are highly in demand for machine learning internships, including roles focused on developing and applying artificial intelligence technologies. Companies seek candidates with knowledge of programming languages like Python, experience with frameworks such as TensorFlow or PyTorch, and understanding of data analysis. The AI industry continues to grow, creating numerous opportunities for interns with relevant skills and training.
What job categories do people searching Internship German Machine Learning jobs in Bryan, TX look for? The top searched job categories for Internship German Machine Learning jobs in Bryan, TX are:
What cities near Bryan, TX are hiring for Internship German Machine Learning jobs? Cities near Bryan, TX with the most Internship German Machine Learning job openings:
Internship German Machine Learning jobs near you

Postdoctoral Research Associate

Tamus

College Station, TX • Hybrid

$4.1K/mo

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 10 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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