Vacancies – AI Lab
Vacancies
Research Assistants
Two research assistants to work on piloting and improving an AI-based mobile
The project:
In this project, a mobile app has been developed to collect and analyze human activity data. The data are collected from a sensor put on the ankle of a person.The current version of the mobile application can learn a model from the sensor data using an AI algorithm and then classify the movement data collected later utilizing the model. The mobile app has been piloted with a healthy person and has shown high accuracy of classification.
Tasks:
The mobile application is expected to be used by stroke patients to guide them to perform the rehabilitation exercises at home. The tasks of this project are to pilot the mobile application to classify the possible movements to be made by the stroke patients and to improve the AI algorithms and the usability of the mobile app. The concrete tasks are:
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Evaluate the accuracy of the current version of the mobile application by simulating the exercises to be performed by possible stroke patients
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Evaluate other options of placing more sensors in different places of the body to learn the activity model and to classify the activities
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Improve the AI algorithm based on the evaluation results
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Improve the usability of the mobile application based on feedback from the stroke patients and doctors
Prerequisites:
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AI or data analysis knowledge
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Experience of Java-based mobile application development
Duration and plan:
The project will need two research assistants, who can work 120 to 180 hours. We expect the studies to be performed in Autumn 2020 and Spring 2021.
Application deadline 10th Oct. 2020. Contact person: Associate professor Jingyue (Bill) Li.
PhD Positions
2 PhD Research Fellows in machine-learning/signal processing for Industry 4.0
The positions are linked to the project SIGNIFY which is funded by the Research Council of Norway. Big data, Internet of Things (IoT) and artificial intelligence (AI) represent key enablers of the digital transformation and the development of digital twins. The main objective 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.
- PhD Position N.1 – Model-Based Sensor Validation
- PhD Position N.2 – Data-Driven Sensor Validation
Learn more about the positions and desired competences here. Deadline: October 10.
Two PhD Research Fellows in machine learning for irregular time series
The Faculty of Information Technology and Electrical Engineering has a vacancy for 2 PhD Research Fellows in machine learning for irregular time series (one with the Department of Mathematical Sciences and one with the Department of Electronic Systems).
Each successful candidate will be offered a 4-year position with 25% work assignments for the corresponding Department.
The positions are linked to the project ML4ITS which is funded by the Research Council of Norway. Time-series data have become pervasive due to the broad diffusion and adoption of Internet of Things (IoT) and major advances in sensor technology. These technologies have applications in several domains, such as healthcare, finance, meteorology and transportation. The main objective of ML4ITS is to overcome the limited availability of (labelled) data for multivariate time-series modelling, where noise and data heterogeneity (e.g. non-stationarity, multi-resolution, irregular sampling) pose critical challenges.
Learn more about the positions here. Deadline: October 10.