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Machine Learning Biomedical Internship Jobs in Alabama

Safety Intern - Spring 2027

Birmingham, AL · On-site

$14 - $18.75/hr

... large machinery, and specialty rubber products. For more than a century, AMERICAN has been ... Paid Internship/Co-op * Housing Stipend * Paid Vacation Day

... learning opportunities, and successful transitions into internships and employment. WHY ATHENS ... Frequently uses hands/fingers to operate a computer and other office productivity machinery, such ...

Controls Engineering Leader

Mccalla, AL

$75K - $97K/yr

... machine control onboarding and ongoing development systems for plant Process Engineers, Interns and ... Experience learning and integrating new technologies * Experience leading the installation of ...

... learning, training, and language courses * A "you" culture where everyone-from interns to ... fax machines. Operation of a vehicle. The employee is regularly required to talk or hear. The ...

Showing results 41-56

Machine Learning Biomedical Internship information

What is a machine learning biomedical internship?

A Machine Learning Biomedical Internship is a temporary position where students or recent graduates work with professionals to apply machine learning techniques in the biomedical field. Interns typically assist with data analysis, model development, and research projects that involve biological or medical data. The goal is to gain practical experience in using artificial intelligence to solve healthcare challenges, such as disease prediction, medical imaging, or drug discovery. These internships often require knowledge of programming languages like Python and familiarity with machine learning frameworks. They provide valuable hands-on experience and networking opportunities for those interested in biomedical data science careers.

What types of projects do interns typically work on during a machine learning biomedical internship?

Interns in Machine Learning Biomedical roles often contribute to projects involving the development and validation of algorithms for analyzing medical data, such as imaging, genomics, or electronic health records. They may assist with data preprocessing, model training, and performance evaluation under the guidance of experienced researchers or engineers. Collaboration is common, as interns often work closely with interdisciplinary teams including data scientists, clinicians, and software engineers. This hands-on experience provides valuable exposure to real-world biomedical challenges while strengthening both technical and communication skills.

What are the key skills and qualifications needed to thrive as a machine learning biomedical intern, and why are they important?

To excel as a Machine Learning Biomedical Intern, you need a solid background in computer science, statistics, and biology, often supported by coursework or a degree in related fields. Familiarity with programming languages like Python or R, experience with machine learning libraries (such as TensorFlow or scikit-learn), and knowledge of data analysis tools are typically required. Strong problem-solving skills, attention to detail, and the ability to communicate complex technical concepts clearly are crucial soft skills. These competencies enable interns to develop effective models, collaborate with multidisciplinary teams, and contribute meaningful insights to biomedical research projects.

What are popular job titles related to Machine Learning Biomedical Internship jobs in Alabama?

For Machine Learning Biomedical Internship jobs in Alabama, the most frequently searched job titles are:

What job categories do people searching Machine Learning Biomedical Internship jobs in Alabama look for?

The top searched job categories for Machine Learning Biomedical Internship jobs in Alabama are:

What cities in Alabama are hiring for Machine Learning Biomedical Internship jobs?

Cities in Alabama with the most Machine Learning Biomedical Internship job openings:

Infographic showing various Machine Learning Biomedical Internship job openings in Alabama as of June 2026, with employment types broken down into 30% Internship, 37% Part Time, and 33% Temporary. Highlights an 100% In-person job distribution.

Postdoctoral Fellow - 530119

University of Alabama

Tuscaloosa, AL • On-site

$53K - $66K/yr

Full-time

Posted 22 days ago


University Of Alabama rating

7.1

Company rating: 7.1 out of 10

Based on 60 frontline employees who took The Breakroom Quiz

411th of 620 rated colleges and universities


Job description

Pay Grade/Pay Range: Minimum: $53,500 - Midpoint: $66,900 (Salaried E8)
Department/Organization: 214251 - Electrical and Computer Eng
Normal Work Schedule: Monday - Friday 8:00am to 5:00pm
Job Summary: The Postdoctoral Fellow provides for an internship and continuation of scholarly activity and research after achieving the PhD or other doctoral degree under the direction of a senior faculty member who serves as a mentor for the postdoctoral appointee.
Additional Department Summary: The Department of Electrical and Computer Engineering at The University of Alabama is seeking candidates for a Postdoctoral position. Research interests and experience in the areas of power electronics, controls systems, and battery management systems.
Successful candidates will hold a Ph.D. degree in Electrical or Computer Engineering or a related field and demonstrate a record of academic achievement in research. Initial appointments are for one year, with renewal contingent availability of funding.
Required Department Minimum Qualifications: Ph.D. in Electrical and Computer Engineering or related field.
Candidates will need to have completed their Ph.D. or have it completed by the start of employment.
Skills and Knowledge: Strong knowledge and hands-on experience in power electronics, control systems, and battery management systems, including the design, development, testing, and validation of power electronic hardware (e.g., DC/DC converters, high-power systems, and electric vehicle applications). Experience with laboratory experimentation, hardware prototyping, system integration, instrumentation, and real-time control implementation is required.
Demonstrated ability to develop and validate control algorithms, perform data acquisition and analysis, and work with embedded systems and digital controllers. Familiarity with modeling, simulation, and experimental validation of energy systems is expected.
Preferred Qualifications: Experience with artificial intelligence and machine learning for energy systems, including data-driven modeling, real-time inference, and integration of AI with control systems. Familiarity with training, validation, and deployment of AI models (e.g., for SoC/SoH estimation, power allocation, or scheduling) is highly desired. Knowledge of robustness, uncertainty quantification, and basic cybersecurity considerations (e.g., data integrity, adversarial inputs, and secure control pipelines) in AI-enabled control systems is a plus.
Background Investigation Statement: Prior to hiring, the final candidate(s) must successfully pass a pre-employment background investigation and information obtained from social media and other internet sources. A prior conviction reported as a result of the background investigation DOES NOT automatically disqualify a candidate from consideration for this position. A candidate with a prior conviction or negative behavioral red flags will receive an individualized review of the prior conviction or negative behavioral red flags before a hiring decision is made.
Equal Employment Opportunity: The University of Alabama is an Equal Employment/Equal Educational Opportunity Institution. All qualified applicants will receive consideration for employment or volunteer status without regard to any legally protected basis and will not be discriminated against because of their protected status. Applicants and employees of this institution are protected under Federal law from discrimination on several bases. More information is available in the EEOC's Know Your Rights: Workplace discrimination is illegal poster.
The University of Alabama affirms its longstanding commitment to institutional neutrality, free speech, and academic freedom.

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