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Paid Internship
Work Mode
Time Spent
Required Degree
Duration

11Open Positions

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Intern/Master Thesis Student for Safe Generative AI

Huawei Austria
Found 1 month ago
Location
Munich, Germany
Duration (Months)
1.5 Months
Time
Not disclosed
Work Mode
Not disclosed
Salary
Not disclosed
Visa Help
Not disclosed
Last Verified
1 month ago

Education

  • Master
  • PhD
  • Bachelor

Skills & Qualifications

Technical Skills

  • Deep Neural Networks (DNN)
  • Vision Transformers (ViT)
  • Data-Intensive Text (DiT)
  • statistics
  • probability
  • linear algebra
  • calculus
  • Python
  • JAX
  • PyTorch

Soft Skills

  • communication skills
  • team environment
  • problem-solving skills

Job Description

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

Requirements

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

Related Field

  • AI & Machine Learning

Related Subfield

  • AI Research

Languages

  • English

Nice to Haves

  • Safe AI for DNN
  • Vision Transformers (ViT)
  • Diffusion Transformer (DiT)
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