The Job

Master thesis: Latent Neural-Operator Structural Causal Models

Teknik, data och digitalt · Data, AI och analys · Maskininlärning · Datavetenskap

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I korthet

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.

Ansvarsområden

  • 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.

Krav

  • 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.

Önskade kvalifikationer

  • 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
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Företag

Ericsson

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för 1 dag sedan

Anställningstyp

Praktik

Arbetsform

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Erfarenhetsnivå

Student

Platser

Stockholm, Sweden

Kvalifikation

Masterexamen

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