About this position
This project is a collaboration between Integreat, the Bosch Centre for Artificial Intelligence (Germany) and the Department of Mathematics and Statistics, University of Lancaster (UK).
The selected applicant will be employed at Bosch Centre for Artificial Intelligence (Germany) and will be enrolled in the PhD programme of the Faculty of Mathematics and Natural Sciences at the University of Oslo, and must adhere to all requirements of this PhD programme. It is a requirement to spend minimum 18 months during your PhD at University of Oslo, the actual time for your stays will be agreed upon individually.
The applicants will be evaluated by all partners. As part of the recruitment and evaluation process, shortlisted candidates will be invited to visit Bosch before possible employment.
Description
Anomaly detection is the problem of detecting and locating points or regions - temporal intervals or subsets of features - in data that behave differently compared to some baseline behaviour. It is important to detect anomalies because they often indicate unexpected events of interest or behavioural patterns in complex systems such as manufacturing environments, finance and health. Automated, quick, customisable and reliable methods for anomaly detection are therefore in substantial and increasing demand. The PhD project will in particular focus on
-
Online/real-time anomaly detection in data streams, including challenging scenarios when the baseline structure is evolving, potentially undergoing concept drift or being time-dependent.
-
Online classification of anomalous-type, by exploring ways in which contemporary machine learning based classification methods like neural nets or autoencoders can be deployed to classify anomalous regions in real time.
We will develop new statistical and machine learning methodology and theory, together with efficient algorithms implemented and evaluated in challenging real-world settings at Bosch, with the possibility to be considered as industrial standard.
Project specific requirements
-
Master’s degree in statistics, mathematics, computer science or a related quantitative subject with proven competence in statistics and/or machine learning.
-
Genuine interest in methodological research
-
Documented experience in scientific programming is necessary
Bosch offers:
- Competitive salary
- Access to travel budget for attending national and international conferences, schools, workshops, etc
- A unique research environment at Integreat and at Bosch with multiple opportunities to develop research themes at the forefront of modern science.
- A friendly professional and stimulating international working environment at Integreat and at Bosch .
- Access to a network of top-level national and international collaborators at Integreat and at Bosch .
- A vibrant international academic environment at Integreat and at Bosch.
- Career development programmes at Bosch and UiO and individual professional plan for the full duration of the doctoral research period.
- Funds through Integreat for shorter research mobility.
- Oslo’s and Stuttgart's family-friendly surroundings with their rich opportunities for culture and outdoor activities.
Supervisors
-
Professor Ingrid K. Glad, UiO, glad@math.uio.no contact person for inquiries about this position
-
Associate Professor Evgeny Kharlamov, UiO and Bosch
-
Professor Idris Eckley, University of Lancaster