• Participating in research within the field of Advanced Architectures such as Transformers, Recurrent Models, and Large Language Models (LLM).
• Conducting research and implementing innovative modifications using TensorFlow, PyTorch, and Keras.
• Rethinking and rebuilding the core mechanisms of models to enhance performance and efficiency.
• Integrating state-of-the-art techniques such as attention mechanisms, sequence modeling, and neural network optimization.

• Possess a Master's degree in Computer Science, Data Science, or a related field, demonstrating a solid foundation in technical knowledge.
• Proficient in Python programming, showcasing strong coding skills including the development and substantial modifications of module classes in TensorFlow and Keras.
• Ability to understand complex problems and solving them efficiently.
• Familiarity with state-of-the-art techniques in deep learning
• Knowledge of Large Language Model (LLM) architectures, such as Generative Pre-trained Transformer models, is a plus.
• Willingness to learn and work with advanced machine learning development stacks
• Exhibits a work ethic that is diligent, responsible, structured, goal-oriented, and independent.
• Good communication abilities and interpersonal skills.
• Proficiency in English.
• Proficiency in German is a plus.

Our research at the Chair of Data Science and Natural Language Processing at the Institute of Computer Science (ICS-HSG) focuses on the areas of large language models (LLM) and quantitative data science, with a specific emphasis on developing next-generation AI deep learning architectures. Our team conducts cutting-edge research to enhance the performance and efficiency of these models, integrating state-of-the-art techniques such as attention mechanisms and sequence modeling. Using tools like TensorFlow, PyTorch, and Keras, we tackle complex problems in AI, aiming to push the boundaries of what's possible in machine learning and data analysis.

Please apply through the job link. Applications will be evaluated continuously.