PhD Fellow: Diffusion-Based Time-Series Anomaly Detection

Högskolan i Halmstad; Halmstads kommun; On-site; SEK 357,000 - 458,000; Full time

Job summary

Halmstad University’s School of Information Technology invites applications for a doctoral student position to investigate diffusion-based anomaly-detection methods within time-series foundation-model frameworks. The project contributes to ENGADE and UNIFY-AI initiatives and requires a focused ML profile and robust programming skills.

You will publish in peer-reviewed venues, complete doctoral coursework, and may undertake teaching/supervision duties up to 20% as part of your doctoral training.

Qualifications

• M.Sc. degree (or equivalent) in computer science, computer engineering, electrical engineering with focus on machine learning and AI.
• Strong communication skills in English.
• Strong knowledge of machine learning, artificial intelligence, data mining, or signal processing.
• Excellent programming skills.
• Analytical problem solving and organizational abilities.

Responsibilities

• Research diffusion-based anomaly detection within time-series foundation-model framework.
• Publish research findings in peer-reviewed venues.
• Complete relevant doctoral coursework as part of the doctoral programme.
• Teaching and supervision duties (up to 20%) can be included.

Skills

Machine learning

Artificial intelligence

Data mining

Signal processing

Programming

Education

M.Sc. degree in CS/CE/EE with ML/AI focus

Job description

Halmstad University’s School of Information Technology invites applications for a doctoral student position to investigate diffusion-based anomaly-detection methods within time-series foundation-model frameworks. The project contributes to ENGADE and UNIFY-AI initiatives and requires a focused ML profile and robust programming skills.

You will publish in peer-reviewed venues, complete doctoral coursework, and may undertake teaching/supervision duties up to 20% as part of your doctoral training.