Master Thesis Researcher: Deep Learning and Graph Neural Networks
hace 3 días
We are seeking a highly motivated Master Thesis Researcher to join our team at Kollmorgen Automation Ab. The successful candidate will have the opportunity to apply Deep Learning to a real-world problem, researching novel representation of graph data.
- Collaborate with domain experts to represent traffic rules in a graph-like data structure.
- Construct training datasets by modifying real AGV system layouts and augmenting with synthetic data.
- Experiment with deep learning architectures, focusing on Graph Neural Networks.
- Perform model selection and hyperparameter tuning.
- Evaluate model robustness and reliability using techniques like K-fold cross-validation.
About You
- Hold a Bachelor Degree in Computer Science, Applied Mathematics or similar.
- Enrolled in relevant master program such as Algorithms, Engineering Mathematics, Complex Adaptive System or Data Science/AI.
- Completed courses in Machine Learning and statistics.
- Had previous work experience as programmer.
- Had experience of Python, including libraries such as numpy, pandas, sklearn and pytorch/tensorflow.
Working with Us
- We will provide computers and great support from your supervisor and other colleagues.
- All students are compensated after the thesis is submitted and the final work meets the quality standard that is generally accepted by the university and presented at Kollmorgen.
- Send in your application by submitting a CV and/or information about your studies and a short sentence about why you find this project interesting.
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