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AI Research Intern(m/w/x)
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.
Anforderungen
- 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
Aufgaben
- 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
Ausbildung
- Laufendes StudiumODER
- Bachelor-AbschlussODER
- Master-Abschluss
Sprachen
- Englisch – verhandlungssicher
Tools & Technologien
- PyTorch
- CNNs
- Transformers
- ONNX
- TensorRT
- TFLite
- LiteRT
- W&B
- MLflow
Benefits
Sonstige Vorteile
- Hands-on research experience
- Exposure to model optimisation
- Experience reading research
- Experience implementing research
- Experience evaluating research
Abwechslungsreiche Aufgaben
- Exposure to edge deployment challenges
Mentoring & Coaching
- Mentorship from researchers
- Mentorship from engineers
Sinnstiftende Arbeit
- Opportunity to contribute to publications
- Opportunity to contribute to conference submissions
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AI Research Intern(m/w/x)
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.
Anforderungen
- 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
Aufgaben
- 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
Ausbildung
- Laufendes StudiumODER
- Bachelor-AbschlussODER
- Master-Abschluss
Sprachen
- Englisch – verhandlungssicher
Tools & Technologien
- PyTorch
- CNNs
- Transformers
- ONNX
- TensorRT
- TFLite
- LiteRT
- W&B
- MLflow
Benefits
Sonstige Vorteile
- Hands-on research experience
- Exposure to model optimisation
- Experience reading research
- Experience implementing research
- Experience evaluating research
Abwechslungsreiche Aufgaben
- Exposure to edge deployment challenges
Mentoring & Coaching
- Mentorship from researchers
- Mentorship from engineers
Sinnstiftende Arbeit
- Opportunity to contribute to publications
- Opportunity to contribute to conference submissions
Über das Unternehmen
Harmattan AI
Branche
Aerospace
Beschreibung
The company builds autonomous and scalable defense systems driven by engineering developments in robotics and AI.
Noch nicht perfekt?
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Computer Vision Engineer (VIO)(m/w/x)
Vollzeitnur vor OrtBerufserfahrenLausanne - Harmattan AI
Machine Learning and State Estimation Intern(m/w/x)
VollzeitPraktikumnur vor OrtLausanne - Harmattan AI
Computer Vision Engineer(m/w/x)
Vollzeitnur vor OrtBerufserfahrenLausanne