Harmattan AI
AI & Computer Vision Intern(m/w/x)
Full-timeInternshipOn-site
Lausanne
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HAHarmattan AI
AI Research Intern(m/w/x)
Lausanne
Full-timeInternshipOn-site
AI/ML
Data Science
Nejo AI Summary
Designing and implementing deep learning models for computer vision, researching CNNs and Vision Transformers. Solid deep learning foundation, preferably PyTorch, required. Hands-on research experience with exposure to model optimization.
Requirements
- Solid Deep Learning foundation (preferably PyTorch)
- Experience with CNNs or Transformers (academic or project-based)
- Understanding of bias–variance trade-offs and generalisation
- Familiarity with optimisation fundamentals and basic probability
- Experience or strong interest in model compression
- Interest in hardware-aware and efficient model design
- Experience with ONNX, TensorRT, TFLite or LiteRT
- Familiarity with experiment tracking tools (e.g., W&B, MLflow)
- Experience conducting ablation studies
- Exposure to dataset curation or annotation processes
- Prior participation in research projects or conference work
Tasks
- Design deep learning models for computer vision
- Implement deep learning models for computer vision
- Research CNNs and Vision Transformers
- Experiment with CNNs and Vision Transformers
- Apply model compression techniques
- Utilize knowledge distillation
- Perform quantisation-aware training (QAT)
- Conduct post-training quantisation (PTQ)
- Implement network pruning strategies
- Implement dataset pruning strategies
- Design efficient architectures for edge systems
- Design efficient architectures for embedded systems
- Curate datasets
- Balance datasets
- Mitigate dataset bias
- Design experiments
- Conduct ablation studies
- Apply reproducibility practices
- Evaluate using appropriate metrics
- Analyze failure cases
- Test robustness under distribution shifts
- Read ideas from leading conferences
- Analyze ideas from leading conferences
- Implement ideas from leading conferences
Education
- Currently in higher educationOR
- Bachelor's degreeOR
- Master's degree
Languages
- English – Business Fluent
Tools & Technologies
- PyTorch
- CNNs
- Transformers
- ONNX
- TensorRT
- TFLite
- LiteRT
- W&B
- MLflow
Benefits
Other Benefits
- Hands-on research experience
- Exposure to model optimisation
- Experience reading research
- Experience implementing research
- Experience evaluating research
Diverse Work
- Exposure to edge deployment challenges
Mentorship & Coaching
- Mentorship from researchers
- Mentorship from engineers
Purpose-Driven Work
- Opportunity to contribute to publications
- Opportunity to contribute to conference submissions
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of Harmattan AI and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
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HAHarmattan AI
AI Research Intern(m/w/x)
Lausanne
Full-timeInternshipOn-site
AI/ML
Data Science
Nejo AI Summary
Designing and implementing deep learning models for computer vision, researching CNNs and Vision Transformers. Solid deep learning foundation, preferably PyTorch, required. Hands-on research experience with exposure to model optimization.
Requirements
- Solid Deep Learning foundation (preferably PyTorch)
- Experience with CNNs or Transformers (academic or project-based)
- Understanding of bias–variance trade-offs and generalisation
- Familiarity with optimisation fundamentals and basic probability
- Experience or strong interest in model compression
- Interest in hardware-aware and efficient model design
- Experience with ONNX, TensorRT, TFLite or LiteRT
- Familiarity with experiment tracking tools (e.g., W&B, MLflow)
- Experience conducting ablation studies
- Exposure to dataset curation or annotation processes
- Prior participation in research projects or conference work
Tasks
- Design deep learning models for computer vision
- Implement deep learning models for computer vision
- Research CNNs and Vision Transformers
- Experiment with CNNs and Vision Transformers
- Apply model compression techniques
- Utilize knowledge distillation
- Perform quantisation-aware training (QAT)
- Conduct post-training quantisation (PTQ)
- Implement network pruning strategies
- Implement dataset pruning strategies
- Design efficient architectures for edge systems
- Design efficient architectures for embedded systems
- Curate datasets
- Balance datasets
- Mitigate dataset bias
- Design experiments
- Conduct ablation studies
- Apply reproducibility practices
- Evaluate using appropriate metrics
- Analyze failure cases
- Test robustness under distribution shifts
- Read ideas from leading conferences
- Analyze ideas from leading conferences
- Implement ideas from leading conferences
Education
- Currently in higher educationOR
- Bachelor's degreeOR
- Master's degree
Languages
- English – Business Fluent
Tools & Technologies
- PyTorch
- CNNs
- Transformers
- ONNX
- TensorRT
- TFLite
- LiteRT
- W&B
- MLflow
Benefits
Other Benefits
- Hands-on research experience
- Exposure to model optimisation
- Experience reading research
- Experience implementing research
- Experience evaluating research
Diverse Work
- Exposure to edge deployment challenges
Mentorship & Coaching
- Mentorship from researchers
- Mentorship from engineers
Purpose-Driven Work
- Opportunity to contribute to publications
- Opportunity to contribute to conference submissions
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of Harmattan AI and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
About the Company
Harmattan AI
Industry
Aerospace
Description
The company builds autonomous and scalable defense systems driven by engineering developments in robotics and AI.
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