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Internship Graduate Machine Learning Jobs in Tennessee

Varsity Tutors

Data Science Tutor

Nashville, TN · Remote

$40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ... graduate programs. * Conceptual Teaching & Problem-Solving: Skilled at teaching the full data ...

Varsity Tutors

Data Science Tutor

Murfreesboro, TN · Remote

$40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ... graduate programs. * Conceptual Teaching & Problem-Solving: Skilled at teaching the full data ...

Varsity Tutors

Data Science Tutor

Chattanooga, TN · Remote

$40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ... graduate programs. * Conceptual Teaching & Problem-Solving: Skilled at teaching the full data ...

Varsity Tutors

Data Science Tutor

Memphis, TN · Remote

$40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ... graduate programs. * Conceptual Teaching & Problem-Solving: Skilled at teaching the full data ...

Varsity Tutors

Data Science Tutor

Knoxville, TN · Remote

$40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ... graduate programs. * Conceptual Teaching & Problem-Solving: Skilled at teaching the full data ...

Varsity Tutors

Data Science Tutor

Kingsport, TN · Remote

$40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ... graduate programs. * Conceptual Teaching & Problem-Solving: Skilled at teaching the full data ...

Church Health

Service Learning Coordinator

Memphis, TN · On-site

$47.14K - $55.46K/yr

Manages external postings of Internship, Scholar, and Graduate service-learning placements on college/university recruiting platforms. * Coordinates applicant review and selection process with ...

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Internship Graduate Machine Learning information

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

To thrive as an Internship Graduate in Machine Learning, you typically need a strong background in mathematics, programming (especially Python), and familiarity with algorithms and data structures, often supported by coursework or a degree in computer science, statistics, or a related field. Hands-on experience with machine learning frameworks like TensorFlow or PyTorch, and knowledge of tools such as Jupyter Notebooks and version control systems like Git, are highly valued. Curiosity, problem-solving, teamwork, and effective communication are crucial soft skills to excel in collaborative and innovative environments. These competencies enable interns to contribute to real-world projects, adapt to fast-changing technologies, and communicate findings clearly within interdisciplinary teams.

What types of projects do Internship Graduate Machine Learning roles typically involve, and how are responsibilities structured within the team?

Internship Graduate Machine Learning roles often focus on supporting ongoing research or development projects, such as building predictive models, cleaning and analyzing data, or prototyping algorithms. Interns usually collaborate closely with data scientists and engineers, contributing to specific project milestones while learning best practices in model development and deployment. Responsibilities are often structured to allow for mentorship and feedback, with interns participating in regular team meetings, code reviews, and brainstorming sessions. This collaborative environment provides valuable exposure to real-world machine learning workflows and helps interns build both technical and soft skills relevant to the field.

What are Internship Graduate Machine Learning positions?

Internship Graduate Machine Learning positions are entry-level roles designed for recent graduates or students who have completed coursework in machine learning, data science, or related fields. These internships provide hands-on experience working with real-world data, building and testing machine learning models, and collaborating with experienced professionals. Interns gain exposure to industry-standard tools and techniques, helping them bridge the gap between academic learning and practical application. Such positions are valuable for building a portfolio, networking, and enhancing job prospects in the rapidly growing field of artificial intelligence.

What is the difference between Internship Graduate Machine Learning vs Data Analyst?

AspectInternship Graduate Machine LearningData Analyst
Required CredentialsDegree in Computer Science, Data Science, or related field; basic knowledge of programming and statisticsDegree in Statistics, Mathematics, or related field; proficiency in data visualization and analysis tools
Work EnvironmentTech companies, research labs, startups; project-based, collaborative teamsBusiness, finance, marketing sectors; focus on reporting and data interpretation
Employer & Industry UsageUsed in tech, AI, and research industries for developing machine learning modelsCommon in corporate, finance, and consulting firms for data-driven decision making

While both roles involve working with data, an Internship Graduate Machine Learning focuses on developing algorithms and models using programming skills, often in tech environments. In contrast, a Data Analyst emphasizes interpreting data, creating reports, and supporting business decisions. The roles overlap in data handling but differ in technical depth and application focus.

What are popular job titles related to Internship Graduate Machine Learning jobs in Tennessee? For Internship Graduate Machine Learning jobs in Tennessee, the most frequently searched job titles are:
What job categories do people searching Internship Graduate Machine Learning jobs in Tennessee look for? The top searched job categories for Internship Graduate Machine Learning jobs in Tennessee are:
What cities in Tennessee are hiring for Internship Graduate Machine Learning jobs? Cities in Tennessee with the most Internship Graduate Machine Learning job openings:
Internship Graduate Machine Learning jobs near you
Post-Doctoral Research Associate: Department of Nuclear Engineering - UTK

Post-Doctoral Research Associate: Department of Nuclear Engineering - UTK

The University of Tennessee

Knoxville, TN

$70K - $75K/yr

Full-time

Posted 15 days ago

Job description

Post-Doctoral Research Associate - Computational Materials Science and Machine Learning

This position involves computational research on nuclear materials, with an emphasis on f-elements (lanthanides and actinides), molten salts, interfacial science, and energy-related processes within a multi-scale modeling framework. The main task focuses on developing and using machine learning interatomic potentials, advanced free energy simulations, and high-performance computing to predict chemical and materials properties, such as thermochemical behaviors and interfacial interactions relevant to nuclear applications. Duties include technical interactions with undergraduate and graduate students, contributing to and developing new research initiatives, writing peer-reviewed scientific journal articles, and technical management of research projects. 

The appointment would be a limited term appointment, approximately 1 year with the possibility of renewal for future years. The end goal after the appointment would be for you to secure a permanent position that is compatible with your interest.

The University of Tennessee, Knoxville, has shaped leaders, changemakers, and innovative thinkers since its founding in 1794. The university is home to more than 38,000 students and 10,000 statewide employees-the Volunteers-who uphold the university's tradition of lighting the way for others through leadership and service. 

UT Knoxville offers over 900 programs of study across 14 degree-granting colleges and schools. As Tennessee's flagship land-grant university, its footprint spans the entire state. The university holds the highest Carnegie classification for research activity and has deep partnerships with industry leaders and the US Department of Energy's largest multidisciplinary laboratory, Oak Ridge National Laboratory. 

The Knoxville campus serves and recruits for UT Knoxville, including the Institute of Agriculture and the Space Institute, as well as the UT Institute of Public Service.  

UT Knoxville considers its employees its number one asset. With values that focus on work-life balance, compensation, and innovation leadership, all Vols are supported to advance professionally. Employees have access to career development and coaching, continued education, and an extensive list of development and training possibilities. The Volunteer employee experience implements structures and practices to attract and retain top-tier talent, fostering a strong staff community and supporting a culture of involvement and engagement for everyone. 

The university holds a strong commitment to its land-grant mission of learning and engagement, with a tradition of service and leadership that carries that Volunteer spirit throughout the state and around the world. It has been ranked nationally as "Best Employer for New Graduates," "One of America's Best Large Employers," and "Best Workplace for Women," and has been designated as "Best Place for Working Parents" by Forbes Magazine.  

Apply today and join the Tennessee Volunteer community! 

Required Qualifications: 

Education: 

  • Ph.D. degree in Materials Science, Chemical Engineering, Computational Chemistry, or an equivalent field. 

Experience: 

  • A well-established track record of research in materials science/computational modeling/chemistry. 
  • Experience in employing advanced statistical mechanics methods and machine learning for simulations of materials.

Preferred Qualifications: 

Knowledge, Skills, and Abilities:

  • Programming experience using Python for workflow development, scientific computing, and data analysis and visualization. 
  • Demonstrated knowledge and experience in advanced free energy simulations methods, such as thermodynamic integration, metadynamics, and umbrella sampling. 
  • Experience in developing and utilization of machine learning interatomic potentials to enhance predictive modeling of chemical processes and materials. 
  • Demonstrated experience in modeling solid-liquid interfaces. 
  • Excellent interpersonal skills, oral and written communication skills. 
  • Motivated self-starter with the ability to work independently and to participate creatively in collaborative teams across the laboratory. 
  • Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever changing needs.

Work Location

  • Location: Knoxville, TN
  • Onsite

Compensation and Benefits 

  • Anticipated hiring range: $70,000 - $75,000
  • Find more information on UT Benefits here

Application Instructions

Interested candidates should submit a letter of application, curriculum vitae, and names and addresses of three references. For questions or inquiries please contact Prof. Ivanov, email: aivanov@utk.edu. Review of applications will begin immediately and will continue until the position is filled.

About The Department

The Department of Nuclear Engineering at the University of Tennessee, Knoxville, established in 1957, was the first of its kind and is considered one of the most prestigious in the United States. Its mission is supported by internationally recognized faculty and advanced research programs, enhanced by close ties with institutions such as Oak Ridge National Laboratory, the Y-12 Nuclear Security Complex, UCOR, and the East Tennessee Technology Park, as well as over one hundred nuclear-related companies within fifty miles of Knoxville. The department offers programs, with its graduate program consistently ranked among the top in the nation by U.S. News and World Report.

Development and evaluation of statistical and machine learning tools for designing and understanding functional materials using the leadership class high performance computing facilities. Work with multi-disciplinary teams to apply modeling and simulation techniques to bulk materials and interfaces, supporting both fundamental research and applied programs. Publishing papers in high-quality refereed journals. Actively collaborating with industry, academia, government labs, and applications developers in a variety of venues.

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