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CACarl Zeiss AG

Internship – AI for Neural Signal Processing in Healthcare Innovation(m/w/x)

Karlsruhe
Full-timeInternshipOn-site
AI/ML
Data Science

Developing ML algorithms for AI-driven medical applications, with hands-on neural signal processing. Advanced Python and PyTorch experience required. Flexible hours, modern office culture.

Requirements

  • Bachelor's degree and current enrollment in master's program in CS, biomedical engineering, physics, electrical engineering, mathematics, mechanical engineering, or related field with ML focus
  • Advanced Python programming experience; ideally prior C/C++ experience
  • Hands-on experience with deep learning frameworks like PyTorch
  • Ideally, domain knowledge in neuroscience, electrophysiological data analysis
  • Ideally, experience in software engineering and development with Microsoft Azure
  • Excellent organization, problem-solving, and communication skills (English and/or German)
  • Passion for innovation and enthusiasm for new technologies; motivation to work in agile, interdisciplinary teams

Tasks

  • Develop and integrate machine learning algorithms
  • Support concept work for AI-driven medical applications
  • Gain hands-on experience with neural signal modalities
  • Analyze and research core functionalities for neuroscience
  • Test and implement multi-modal data solutions
  • Research scientific and patent literature
  • Tailor solutions to project needs
  • Conduct experiments on large datasets
  • Evaluate experiment results

Education

  • Currently in higher educationOR
  • Bachelor's degree

Languages

  • EnglishBusiness Fluent
  • GermanBusiness Fluent

Tools & Technologies

  • Python
  • C/C++
  • PyTorch
  • Microsoft Azure

Benefits

Informal Culture

  • Modern office culture
  • Dynamic, motivated, diverse team

Flexible Working

  • Flexible working hours

Startup Environment

  • Agile work environment
  • Large and active student community
  • Conceptual work for low-TRL proof-of-concepts
  • Early product development

Diverse Work

  • Possibility to work on various tasks
  • Hands-on experience
  • Large, interdisciplinary research environment
  • Deep insights into practical development work
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