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Physics Informed Machine Learning Intern(m/w/x)
Designing and implementing Physics Informed Neural Networks for manufacturing AI. Master's degree in STEM with ML and differential equations knowledge required. International, diverse work environment with strong mentorship.
Anforderungen
- Master’s degree in Applied Mathematics, Physics, Computer Science, or related field
- Foundations in machine learning and numerical methods
- Familiarity with scientific machine learning concepts
- Experience with Python and ML frameworks in academic or professional environment
- Ability to reason about physical systems, differential equations, or mechanistic models
- Curiosity and motivation to follow AI-for-science advances
- English (professional working proficiency)
Aufgaben
- Design and implement PINN architectures
- Integrate physical constraints into ML models
- Benchmark scientific ML models
- Analyze results and identify limitations
- Iterate model design and training strategies
- Contribute to integrating physics-aware AI models
Ausbildung
- Laufendes Studium
Sprachen
- Englisch – verhandlungssicher
Tools & Technologien
- Python
- PyTorch
- TensorFlow
Benefits
Lockere Unternehmenskultur
- International, diverse, equitable, inclusive work environment
Sinnstiftende Arbeit
- Opportunities to have an impact
Weiterbildungsangebote
- Endless learning opportunities
Sonstige Vorteile
- Enriching experiences
- Accessibility of recruitment process
- Confidential information treatment
Mentoring & Coaching
- Strong mentorship
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Physics Informed Machine Learning Intern(m/w/x)
Designing and implementing Physics Informed Neural Networks for manufacturing AI. Master's degree in STEM with ML and differential equations knowledge required. International, diverse work environment with strong mentorship.
Anforderungen
- Master’s degree in Applied Mathematics, Physics, Computer Science, or related field
- Foundations in machine learning and numerical methods
- Familiarity with scientific machine learning concepts
- Experience with Python and ML frameworks in academic or professional environment
- Ability to reason about physical systems, differential equations, or mechanistic models
- Curiosity and motivation to follow AI-for-science advances
- English (professional working proficiency)
Aufgaben
- Design and implement PINN architectures
- Integrate physical constraints into ML models
- Benchmark scientific ML models
- Analyze results and identify limitations
- Iterate model design and training strategies
- Contribute to integrating physics-aware AI models
Ausbildung
- Laufendes Studium
Sprachen
- Englisch – verhandlungssicher
Tools & Technologien
- Python
- PyTorch
- TensorFlow
Benefits
Lockere Unternehmenskultur
- International, diverse, equitable, inclusive work environment
Sinnstiftende Arbeit
- Opportunities to have an impact
Weiterbildungsangebote
- Endless learning opportunities
Sonstige Vorteile
- Enriching experiences
- Accessibility of recruitment process
- Confidential information treatment
Mentoring & Coaching
- Strong mentorship
Über das Unternehmen
Nestlé Deep Tech Center
Branche
Food
Beschreibung
The company develops cutting-edge technologies to accelerate global innovation in food and enhance quality of life.
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- Nestlé
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