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Master Thesis: AI ML for Anomaly Detection in Baseband System Hardware at Ericsson
Stockholm, , Sweden -
Full Time

Start Date

Immediate

Expiry Date

23 Mar, 26

Salary

0.0

Posted On

23 Dec, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Data Science, Machine Learning, AI Methodologies, Time Series Analysis, Python, Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, Apache Spark, Jupyter, Linux, SQL, Data Visualization, Clustering Techniques

Industry

Telecommunications

Description
Background in data science, machine learning, and AI methodologies. Proficiency in time series analysis and related algorithms. Experience with Python and relevant libraries (e.g., pandas, NumPy, scikit-learn, TensorFlow, PyTorch). Experience with Apache Spark (pyspark), Jupyter, Linux, SQL(PostgreSQL). Familiarity with data visualization techniques and tools, such as Grafana. Basic understanding of clustering techniques and statistical methods. Knowledge of power consumption and temperature measurement data from electronic hardware products is a plus. Ability to work independently and systematically, with a problem-solving mindset.
Responsibilities
The role involves conducting a master thesis focused on AI and machine learning for anomaly detection in baseband system hardware. The candidate will apply various data science methodologies to analyze and interpret data.