Master Thesis: Characterizing hardware imperfection of power amplifiers
Technology, Data & Digital · Data, AI & Analytics · Machine Learning · Data Science · Software Engineering
In short
Responsibilities
- Investigate state-of-the-art research on conventional and neural network-based models for hardware imperfections.
- Suggest suitable models for the imperfection under study.
- Implement models in the existing simulation framework.
- Compare model performance and complexity with existing ones.
- Perform analytical studies on model performance to gain insights into imperfections.
Requirements
- Student in electrical engineering, computer science, machine learning, or similar.
- Background or experience with signal processing.
- Background or experience with deep learning (e.g., convolutional neural networks, Long-short term memory, transformer based).
- Background or experience with radio frequency components is preferred.
Benefits
- Outstanding opportunity to use skills and imagination to push boundaries.
- Chance to build solutions never seen before.
- Challenging environment with a team of diverse innovators.
- Opportunity to craft what comes next.
- Encouraging a diverse and inclusive organization.
- Championing diversity in everything we do.
- Collaboration with people with different experiences drives innovation.
- Ericsson is proud to be an Equal Opportunity Employer.
Skills
Our purpose \nTo create connections that make the unimaginable possible.\n\nOur vision\nA world where limitless connectivity improves lives, redefines business and pioneers a sustainable future.\n\nOur values\nPerseverance, professionalism, respect and integrity.\n\nThe future is a place for purpose & vision – ours are clear, and we invite partners, customers and consumers to join us in our journey. \n\nFor a brighter future. For all. Let's #ImaginePossible
Company
EricssonJob Posted
1 month ago
Employment Type
Internship
Work mode
On Site
Experience Level
Student
Locations
Stockholm, Sweden
Qualification
Applicants
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