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Computer Science Phd Research Intern Jobs in Georgia

Printpack

Analytical Lab Intern

Villa Rica, GA · On-site

Printpack's Analytical Science Team is seeking a motivated and detail-oriented intern to support analytical testing, product investigations, and research activities within our packaging and materials ...

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Computer Science Phd Research Intern information

What are the key skills and qualifications needed to thrive as a Computer Science PhD Research Intern, and why are they important?

To thrive as a Computer Science PhD Research Intern, you need advanced knowledge in computer science fundamentals, research methodology, and typically be enrolled in a relevant PhD program. Experience with programming languages (such as Python, C++, or Java), data analysis tools, and version control systems like Git is often required. Strong analytical thinking, problem-solving abilities, and effective communication skills help interns collaborate and present complex ideas clearly. These skills are essential for contributing to innovative research projects, publishing findings, and succeeding in a competitive academic or industry research environment.

What types of projects and collaborations can a Computer Science PhD Research Intern typically expect during their internship?

As a Computer Science PhD Research Intern, you will often work on advanced research projects that align with your academic interests and the organization's strategic goals. Interns typically collaborate closely with senior researchers, engineers, and sometimes cross-functional teams such as product or data science groups. Projects may involve designing experiments, developing prototypes, or publishing findings in academic venues. The environment is usually supportive of exploration and peer feedback, and you’ll have opportunities to present your work, gain mentorship, and contribute to impactful, real-world applications.

What does a Computer Science PhD Research Intern do?

A Computer Science PhD Research Intern typically contributes to cutting-edge research projects under the guidance of experienced mentors, often in an academic or industry setting. Responsibilities may include designing experiments, developing algorithms, conducting data analysis, and publishing findings in conferences or journals. The role is designed to provide practical research experience, sharpen technical skills, and foster collaboration with other researchers. Interns often have the opportunity to work on real-world problems and gain insights into the research and development process within the field of computer science.
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Computer Science Phd Research Intern jobs near you
Machine Learning Engineer - PhD or PhD Candidate (Near Completion)

Machine Learning Engineer - PhD or PhD Candidate (Near Completion)

Optimal Inc.

Embry Hills, GA • On-site

Contractor

This job post has expired today. Applications are no longer accepted.

Job description


Candidates with only a Master's degree will not be considered.

Minimum qualification: PhD (completed or currently pursuing with expected completion in the near term) in a relevant technical field.

This is an urgent requirement with an anticipated start date within 2 weeks.
Priority will be given to candidates who can interview promptly and begin within two weeks of selection.

We are seeking a highly motivated Machine Learning / Deep Learning Research Engineer with strong expertise in Machine Learning, Deep Learning, Computer Vision, and AI research. This role is intended exclusively for PhD graduates or candidates near completion from reputable universities. Candidates with a strong academic research background in Machine Learning, Artificial Intelligence, Computer Vision, Data Science, or related fields are encouraged to apply.

Research experience gained during a PhD program will be considered equivalent to professional industry experience.

This is an urgent hiring requirement, and we are actively seeking candidates who can start within the next 2 weeks.

Education Requirement

PhD in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, Machine Learning, Data Science, or a related technical field
Candidates currently pursuing a PhD with anticipated graduation within the next 3-6 months are also encouraged to apply
Only PhD candidates will be considered for this role
Candidates with only a Master's degree will not be considered


Key Responsibilities
Design, develop, train, and optimize Machine Learning and Deep Learning models for real-world applications
Own the complete ML lifecycle including data collection, annotation, preprocessing, model training, fine-tuning, evaluation, optimization, and deployment
Develop and deploy advanced deep learning architectures including CNNs, LSTMs, ConvLSTMs, Graph Neural Networks (GNNs), Reinforcement Learning, and Transformer-based models
Conduct experiments, evaluate model performance, and drive continuous algorithmic improvements
Work with large-scale datasets for model training, validation, and testing
Optimize and deploy AI models for scalable and efficient real-world applications
Translate research concepts into scalable, production-ready AI systems
Collaborate with cross-functional engineering and research teams to integrate ML models into real-world applications
Document methodologies, experimental findings, and technical solutions
Contribute to technical innovation initiatives and advanced AI research activities

Required Qualifications
Strong PhD research background in Machine Learning, Deep Learning, Artificial Intelligence, Computer Vision, Data Science, or related areas
Strong programming experience with Python and C++
Hands-on experience with PyTorch, TensorFlow, Keras, Scikit-learn, or similar ML frameworks
Strong understanding of Machine Learning, Deep Learning, Neural Networks, and AI algorithms
Experience developing and training advanced deep learning models and architectures
Solid mathematical foundation in linear algebra, probability, statistics, optimization, and applied machine learning
Experience working with Linux environments, Git, Docker, and modern development workflows
Demonstrated research experience through publications, thesis work, academic research projects, or equivalent research contributions
Strong ability to independently research, prototype, and deploy AI solutions

Preferred Qualifications
Publications in leading AI, Machine Learning, or Computer Science conferences/journals
Experience transitioning AI/ML models from research environments into production systems
Experience with CUDA, GPU acceleration, distributed computing, or high-performance computing
Experience handling large-scale, real-world datasets

This role is ideal for candidates passionate about applying advanced AI research, machine learning, and deep learning techniques to solve challenging real-world problems.

Apply