Applied Sciences INTERN

Design, implement, and evaluate machine learning and AI solutions for real-world product scenarios. Analyse and improve the performance of advanced algorithms on large-scale datasets. Translate business and product challenges into machine learning research problems and experimental frameworks.

Design and execute rigorous experiments to evaluate model effectiveness and drive continuous improvement. Develop, train, fine-tune, and evaluate machine learning models, including Large Language Models (LLMs) and Small Language Models (SLMs). Build scalable prototypes and AI-powered systems that can be deployed in production environments.

Prepare, clean, and analyse large datasets while ensuring data quality and integrity. Develop innovative approaches to model evaluation, benchmarking, and performance optimization. Document research findings, communicate results, and contribute to technical reports, publications, patents, or open-source initiatives.

Currently pursuing a PhD degree in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, Electrical Engineering, Computer Engineering, Econometrics, or a related technical field. Must have at least one semester/quarter remaining following completion of the internship. Research experience in Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Generative AI, or related areas.

Strong programming skills in Python and experience with machine learning frameworks such as PyTorch, TensorFlow, or equivalent. Experience designing experiments, analysing results, and applying statistical methods to solve research problems. Demonstrated ability to formulate hypotheses, conduct independent research, and communicate technical findings effectively.

Experience working with large datasets and building end-to-end machine learning pipelines