Format : Could be both On Campus and Online
Number of Interns : 5
Duration of the Internship : 1 Month
Start Date : 29.06.2026
Finish Date : 24.07.2026
Application Deadline : 29.05.2026
Project Supervisor : Prof. Dr. Hasan F. Ateş
Project Description
Have you ever wondered how scientists can see heat, detect objects in complete darkness, or observe the Earth through clouds? Many modern technologies—such as satellites, autonomous vehicles, and rescue robots—use special sensors like thermal cameras and radar imaging (SAR) to “see” things that our eyes cannot.
In this summer internship, you will explore how Artificial Intelligence can transform ordinary images into entirely different sensing modalities. Using modern computer vision and generative AI techniques, you will develop models that can convert normal optical images into synthetic thermal or radar-like images. In other words, you will teach a computer to imagine how the same scene would look if it were captured by a different type of sensor.
During the program, you will learn the foundations of computer vision, machine learning, and generative AI, and apply them to real-world problems such as remote sensing, environmental monitoring, and intelligent systems. Working in a university-level research team, you will experiment with datasets, test AI models, and evaluate how well your system can translate images between different modalities.
By the end of the internship, you will have built your own AI-powered image translation system and presented your work in a mini research showcase. This is a unique opportunity to experience how cutting-edge AI research is conducted and to discover how computer vision technologies are shaping the future of science and engineering.
No prior experience in artificial intelligence is required—just curiosity, creativity, and a willingness to explore how machines can learn to see beyond human vision.
Research Intern Responsibilities
1. Exploring Image Modalities: Learn about different types of imaging sensors, including optical, thermal, infrared, and SAR. Compare how the same scene appears under different sensing technologies.
2. Using AI Image Generation Tools: Work with pretrained generative AI models for image-to-image translation. Use prompt engineering and conditioning techniques to guide image generation. Generate synthetic images that simulate other sensing modalities.
3. Designing Image Translation Experiments: Test how different prompts, parameters, and conditioning inputs affect the generated images. Explore different scenes such as urban environments, natural landscapes, and buildings.
4. Evaluating Generated Images: Assess the quality and realism of generated images. Compare generated outputs with real examples from thermal or SAR datasets. Identify strengths and limitations of current generative models.
5. Research Documentation and Presentation: Keep a research notebook documenting experiments, prompts, and results. Summarize findings and insights from the experiments. Prepare a research poster or presentation describing the project. Demonstrate generated results during the final internship showcase.
Required Skills and Qualifications
Expected Learning Outcomes
At the end of this internship students will develop the following key skills:
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