Internship in the Development of Physics-Informed Neural Networks PINN for Microfluidic Simulation
Internship in the Development of Physics-Informed Neural Networks PINN for Microfluidic Simulation
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Job Description
- During your internship you will work on the further development of an inhouse software tool for physical modeling in the context of microfluidics and on the development of a PhysicsInformed Neural Networks (PINN) model in PyTorch for simulating microfluidics.
- You will train and evaluate the PINN model using experimental and numerical data and integrate the trained model into the existing software environment.
- Furthermore you will optimize the PINN models performance regarding computation time memory requirements and accuracy.
- Finally you will collaborate within the development team and participate in regular meetings.
Qualifications :
- Education: Master studies in the field of Mechanical Computational Software Engineering or comparable
- Experience and Knowledge: very good in programming and machine learning techniques in Python (preferably with PyTorch); good knowledge in code optimization and testdriven software development; solid understanding of basic physics and math
- Personality and Working Practice: you effectively analyze complex problems work independently to find solutions collaborate well with others and communicate clearly
- Enthusiasm: strong interest in the field of industrial research
- Languages: fluent in German or English
Additional Information :
Start: according to prior agreement
Duration: 6 months
Requirement for this internship is the enrollment at university. Please attach your CV transcript of records enrollment certificate examination regulations and if indicated a valid work and residence permit.
Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore we welcome all applications regardless of gender age disability religion ethnic origin or sexual identity.
Need further information about the job
Alexander Eifert (Functional Department)
49 6
Christian Kuntz (Functional Department)
49
#LIDNI
Remote Work :
No
Employment Type :
Fulltime
Employment Type
Full-time
