Master thesis: Adapting Neural Networks to New Tasks
Technology, Data & Digital · Data, AI & Analytics · Machine Learning · Artificial Intelligence
In short
Join Ericsson Research in Stockholm for a Master's thesis focusing on adapting neural network architectures for dynamic machine learning tasks in telecommunications. Investigate continual learning and neural architecture search to enhance model performance and resilience.
Responsibilities
- Conduct literature review in continual learning, neural architecture search, and machine learning in telecommunications.
- Evaluate existing solutions to establish a baseline.
- Develop and evaluate an approach for adapting neural architectures for improved model performance.
- Collaborate with supervisor to define research directions.
- Collaborate with research team to ensure technical feasibility.
- Present findings through regular discussions and final thesis documentation.
Requirements
- Master's student with most courses completed and strong academic performance.
- Proficiency in Python programming.
- Hands-on experience with machine learning frameworks such as PyTorch.
- Strong programming, debugging, and problem-solving skills, including effective use of generative AI development tools.
- Excellent communication skills.
- Excellent written and spoken English.
- Ability to work as part of an international team.
- Analytical mindset, ability to learn quickly, work independently, and identify problems and solutions.
Desired Qualifications
- Knowledge of continual learning, deep learning, or neural architecture search is a plus.
- Knowledge in telecommunication networks.
Benefits
- Mentorship from experienced researchers at Ericsson Research.
- Access to industry tools and datasets.
- Opportunity to support Ericsson initiatives towards an AI-native network.
- Potential for outstanding results to contribute to scientific publications, patent applications, and future Ericsson research activities.
#AI#machine learning#telecom
Company
Ericsson
Job Posted
2 days ago
Employment Type
Internship
WorkMode
On Site
Experience Level
Student
Locations
Stockholm, Sweden
Qualification
Master
Applicants
Be an early applicant