Intern/Master Thesis Student for Safe Generative AI

Das ist der Job

Overview Huawei is a leading global information and communications technology (ICT) solutions provider.

Darum lohnt es sich

We are seeking a highly motivated and skilled Research Intern to join our dynamic team, focusing on the safety of generative AI, e.g. Collaborate closely with a multidisciplinary team of researchers, data scientists, and engineers to integrate findings into the development of more reliable LLM frameworks.

Demonstrated ability to work collaboratively in a team environment and to engage with external research communities.

You can read in full our recruitment privacy statement via the link below. http://career.huawei.com/reccampportal/portal/hrd/weu_rec_all.html Your rewards of working here Our culture is characterized by innovative power and team spirit as well as the intensive exchange of knowledge and experience within our global network.

Self-responsible work in a competent, motivated and constantly growing team. Our ICT solutions, products and services are used in more than 170 countries and regions, serving over one-third of the world's population. With 197,000 employees, Huawei is committed to develop the future information society and build a Better Connected World.

Huawei\'s Heisenberg Research Center in Munich is responsible for advanced technology research, architectural development, design and strategic engineering of our products. Role Join us as a Research Intern/Master Thesis for Safe Generative AI (m/f/d) . Large Language Models (LLMs).

This internship offers a unique opportunity to contribute to cutting-edge research in the field of artificial intelligence, specifically in understanding, improving, and evaluating the reliability and robustness of the latest generative AI models.

Your mission Conduct comprehensive research on generative AI safety with a focus on assessing, enhancing, and validating their reliability and robustness in various applications. Develop and implement innovative methodologies to test LLM reliability under diverse conditions and datasets.

Analyze and interpret complex data sets, utilizing advanced statistical and machine learning techniques to understand model behaviors and identify potential reliability issues.

Stay abreast of the latest advancements in Safe AI for DNN, Vision Transformers (ViT), and Diffusion Transformer (DiT) etc., applying this knowledge to improve LLM reliability.

Prepare detailed reports and presentations on research findings for both technical and non-technical audiences, contributing to research papers, patents, and other publications as required.

Your areas of expertise Currently enrolled in a Master\'s or PhD program in Computer Science, Artificial Intelligence, Machine Learning, or a related STEM field, Bachelor students with excellent academic records will also be considered.

Solid understanding and hands-on experience with Deep Neural Networks (DNN), Vision Transformers (ViT), Data-Intensive Text (DiT), and other advanced AI/ML models. Strong foundation in the mathematical principles of learning, including but not limited to statistics, probability, linear algebra, and calculus.

Proven ability to conduct independent research and problem-solving skills in the field of AI and machine learning. Proficient in programming languages commonly used in AI research such as Python, and familiar with AI/ML frameworks like JAX or PyTorch.

Excellent communication skills, with the ability to present complex technical information clearly and concisely. By applying to this position, you agree with our RECRUITMENT PRIVACY STATEMENT. We offer healthy meals ranging from traditional Chinese to western delicacies in our famous company canteen.

To keep your development ongoing, you will find a broad range of training opportunities. Many online and face-to-face training programs incl. language courses in German and Mandarin. Our diverse and welcoming environment is shaped by different backgrounds and around 40 individual nationalities.

Please send your application and CV (incl. cover letter and reference letters) in English. #J-18808-Ljbffr