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Paid Internship
Work Mode
Time Spent
Required Degree
Duration

6Open Positions

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  • PHD in Multimodal Artificial Intelligence and On-Edge Deployment (F/M/D)

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PHD in Multimodal Artificial Intelligence and On-Edge Deployment (F/M/D)

CSEM
Found 2 months ago
Location
Neuchâtel, Switzerland
Duration (Months)
100 Months
Time
Full-time
Work Mode
Not disclosed
Salary
100%
Visa Help
Not disclosed
Last Verified
1 month ago

Education

  • Master

Skills & Qualifications

Technical Skills

  • Machine learning algorithms
  • deep learning
  • Python
  • C++
  • TensorFlow
  • PyTorch
  • Multimodal Artificial Intelligence
  • On-Edge Deployment
  • multimodal fusion
  • cross-attention architectures
  • representation learning
  • hardware awareness
  • efficient deployment
  • model generalization
  • imaging
  • sensor signals
  • structured clinical or contextual data
  • temporal information
  • spatio-temporal models
  • embedded training

Soft Skills

  • Natural curiosity
  • adaptability
  • collaborative approach
  • Autonomous
  • innovative thinker
  • problem-solving abilities
  • results oriented
  • Strong organisation
  • communication skills

Job Description

We invite applications for a fully funded PhD position in Multimodal Artificial Intelligence, focusing on the development of next-generation AI methods that integrate diverse data modalities while remaining deployable on resource-constrained hardware platforms. The successful candidate will work on novel multimodal fusion methods, cross-attention architectures, and robust representation learning, with a strong emphasis on hardware awareness, efficient deployment, and model generalization. The project explores how imaging, sensor signals, structured clinical or contextual data, and temporal information can be combined into unified, interpretable, and transferable AI systems. Beyond algorithmic development, the research will investigate how these models behave under domain shifts and how they can be distilled into compact, deployable architectures suitable for edge devices.

Requirements

  • Master’s degree in computer science, Electrical Engineering, Machine Learning, or a related field.
  • Strong background in machine learning algorithms and deep learning.
  • Experience with attention (cross) mechanism
  • Proficiency in programming languages such as Python, C++, and frameworks like TensorFlow or PyTorch.
  • Research Aptitude: demonstrated research experience through coursework or prior projects and show eagerness to engage in research activities and a willingness to learn and contribute to ongoing projects.
  • Academic Excellence: strong academic record, with a history of outstanding performance in relevant coursework.

Related Field

  • AI & Machine Learning

Related Subfield

  • AI Research

Languages

  • English

Nice to Haves

  • Support proposal writing.
  • Supervision of master students.
▶Apply Now

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