en
Nonprofit organization, Browse similar opportunities
The Department of Electronic Systems at NTNU offers 2 PhD research fellowships in machine-learning/signal-processing for Industry 4.0. The successful candidate will be offered a 3-year position (or 4-year with 25% work assignments for the Department).
The positions are linked to the project SIGNIFY which is funded by the Research Council of Norway. Big data, the Internet of Things (IoT), and artificial intelligence (AI) represent key enablers of the digital transformation and the development of digital twins. The main objective of the SIGNIFY is the development and the integration of signal processing and machine learning methodologies into novel hybrid-analytics solutions aiming at sensor validation for digital twins of safety-critical systems. Building upon ground-breaking concepts from graph signal processing, deep learning, and transfer learning, SIGNIFY focuses on designing tailored strategies from a Bayesian perspective and testing them on two use cases related to Carbon Capture and Storage (CCS) in collaboration with SINTEF Energy.
The research plans of the two positions focus on safety issues and lie within the general area of anomaly detection. The PhD candidates will have the opportunity to be affiliated with the IoT@NTNU and with the Norwegian Open AI lab, and to collaborate with research scientists from international partner institutions.
PhD Position N.1 – Model-Based Sensor Validation
The activities related to this position include exploring and defining advanced solutions by integrating domain knowledge related to the considered use cases with linear/nonlinear estimation/detection and data fusion techniques according to a Bayesian framework. A peculiar aspect is represented by considering dynamic risk analysis into the algorithm design to be used with real-time data availability.
PhD Position N.2 – Data-Driven Sensor Validation
The activities related to this position include time-series modeling and monitoring based on shallow and/or deep networks with a focus on soft decisions and related confidence in order to be compatible with a Bayesian framework. A peculiar aspect is represented by considering dynamic risk analysis into the algorithm design to be used with real-time data availability.
This PhD-position's main objective is to qualify for work in research. The qualification requirement is a competition of a master’s degree or second degree (equivalent to 120 credits) with a strong academic background in Machine Learning and or Signal Processing (or equivalent education) with a grade of B or better in terms of NTNU’s grading scale. If you do not have letter grades from previous studies, you must have an equally good academic foundation. If you are unable to meet these criteria you may be considered only if you can document that you are particularly suitable for education leading to a PhD degree.
NTNU seeks two highly-motivated individuals having:
Publication activity in the aforementioned disciplines will be considered an advantage but is not a requirement.
Applicants must be qualified for admission to a PhD study program at NTNU.
Applicants who do not master a Scandinavian language should provide evidence of good written and spoken English language skills. The following tests can be used as documentation: TOEFL, IELTS, Cambridge Certificate in Advanced English (CAE), or Cambridge Certificate of Proficiency in English (CPE). Minimum scores are:
The successful candidates should be:
The Norwegian University of Science and Technology (NTNU) is a public research university in Norway with the main campus in Trondheim and smaller campuses in Gjøvik and Ålesund. The largest university in Norway, NTNU has over 8,000 employees and over 40,000 students. NTNU in its current form was established by the King-in-Council in 1996 by the merger of the former University of Trondheim and other university-level institutions, with roots dating back to 1760, and has later also incorporated some former university colleges. NTNU is consistently ranked in the top one percent among the world's universities, usually in the 101–500 range depending on ranking.
See more scholarships available on Mina7
See more available exchange programs on Mina7
See more opportunities in the UK available on Mina7
Choose the country you wish to travel to study for free, work or volunteer