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Paid Internship
Work Mode
Time Spent
Required Degree
Duration

12Open Positions

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AI Software Research PhD Thesis Intern (m/f/d)

Intel Corporation
Found 1 month ago
Location
Munich, Germany
Duration (Months)
3 Months
Time
Full-time
Work Mode
Hybrid
Salary
Not disclosed
Visa Help
Not disclosed
Last Verified
1 month ago

Education

  • PhD

Skills & Qualifications

Technical Skills

  • computer vision
  • machine learning
  • deep learning
  • AI models
  • hardware/software integration
  • performance analysis tools
  • VTune
  • processor simulators
  • parallel workloads
  • scientific computing
  • data analytics
  • multi-node hardware/software systems
  • roofline analysis
  • communication avoidance
  • replication
  • caching behavior

Soft Skills

  • collaborative
  • supportive environment designed for growth

Job Description

In this role, you will contribute to the design, development, and optimization of AI software solutions, spanning algorithms, frameworks, and AI software architectures. Your work may touch multiple domains, including computer vision, machine learning, and deep learning. You’ll support efforts to implement, tune, and evaluate AI models for both performance and accuracy, participate in applied research, and contribute to hardware/software integration for next‑generation AI systems. This internship may also involve collaborating with internal product teams and external partners to create end‑to‑end AI solutions, as well as working on system‑level deployment to ensure scalable and efficient AI performance. As an intern, you’ll have the opportunity to: * Learn and apply new concepts in real-world scenarios * Build practical skills through hands-on projects * Explore future career paths in AI and software engineering * Contribute to initiatives that support Intel’s business goals * Work in a collaborative, supportive environment designed for growth

Requirements

  • Currently pursuing a PhD in Computer Science or a closely related field
  • Proven experience—through publications, upstream contributions, or similar—in workload analysis, workload optimization, and computer architecture

Related Field

  • AI & Machine Learning

Related Subfield

  • AI Research

Languages

  • English

Nice to Haves

  • performance analysis tools, such as VTune or processor simulators
  • parallel workloads, including machine learning, scientific computing, or data analytics
  • multi-node hardware/software systems, along with familiarity in analysis and optimization strategies such as roofline analysis, communication avoidance, replication, and caching behavior
▶Apply Now

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