Master thesis: Latent Neural-Operator Structural Causal Models
Technology, Data & Digital · Data, AI & Analytics · Machine Learning · Data Science
In short
Ericsson Research is seeking a motivated student for a Master's thesis on Latent Neural-Operator Structural Causal Models. This project involves developing and implementing a causal model for complex telecom systems, evaluating its identifiability and compositional generalization capabilities, and comparing it with existing methods using a link-level simulator.
Responsibilities
- Review causal representation learning and identifiability literature.
- Build a controlled physical-layer simulator with known ground truth.
- Implement the proposed model and comparison baselines.
- Evaluate identifiability and prediction of untested interventions.
- Present findings in regular discussions and the final thesis, including negative results.
Requirements
- A largely completed master’s degree with strong academic performance.
- Knowledge of machine learning, linear algebra, and probability.
- Proficiency in Python and experience with PyTorch or JAX.
- Ability to work independently and use AI coding assistants effectively.
- Excellent written and spoken English and teamwork skills.
Desired Qualifications
- Interest in causal inference or signal processing is beneficial.
- An analytical, research-oriented mindset.
- Ability to learn quickly.
- Initiative to identify problems and solutions.
#Master Thesis#AI#Machine Learning#Causal Inference#Structural Causal Models#Neural Operators#Python#PyTorch#JAX#Sweden
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