Position Overview

KU is seeking a highly motivated Post-doctoral Fellow to work on an externally funded project to investigate the development of a new stretchable and low-cost tactile sensor for robotics applications. The project will specifically focus on the use of a relatively new modality, Electrical Impedance Tomography (EIT). However, reconstruction of EIT images is an ill-posed and ill-conditioned problem. In this research, a modular framework based on machine and deep learning is proposed for image reconstruction, segmentation and automated interpretation of tactile stimuli.

The successful candidate will be responsible for the design and implementation of machine/deep learning algorithms for the solution of the inverse problem in EIT (i.e., image reconstruction), and supporting the integration, testing and validation of the tactile sensing system. Focus will be placed on Investigating novel approached in image reconstruction. For instance, using suitable representation schemes (i.e., transform coding), coupled with machine learning, but also deep learning approaches, e.g., Generative Adversarial Networks (GAN), and designing appropriate regularization functionals.

Position Requirements

• Conduct research on the development of novel image reconstruction methods for EIT tactile sensing.
• Design and simulate both static and dynamic tactile stimuli, e.g., touching, pressing, pulling, tapping, pinching, using state-of-the-art EIT modelling software.
• Design and implement machine learning methods using novel representations for image reconstruction under various experimental setups/configurations.
• Design and implement end-to-end deep learning approaches for image reconstruction under various experimental setups/configurations.
• Demonstrate expertise in the use of high level programming languages (e.g., MATLAB, Python), and be competent in developing software applications for deployment in GPUs/HPC, etc.
• Write independently, as directed by the project supervisor, publications for submission to Q1 journals and premier computer science conferences.
• Take responsibility for reporting technical progress to the project investigator, periodically and in a timely manner.
• Carry out a specified research program under the direction of the project investigator.
• Collaborate and work in a collegial manner with the project team members.
• Function autonomously, ethically, and responsibly.
• Adhere to the University\'s information security and confidentiality policies and procedures, and report breaches or other security risks accordingly
• Perform any other tasks assigned by the Line Manager.

Qualifications

Candidate Profile

Essential Criteria

• PhD from a reputable university in Computer Science, Engineering or related discipline.
• Demonstrable background/experience in machine/deep learning and the broad area of AI.
• Experience in some of the following areas: Electrical Impedance Tomography, Sensor Signal Processing, Image Analysis, Inverse Problems.
• Demonstrable ability to develop software code in MATLAB/Python, including running applications in GPUs or HPC.
• Experience writing scientific or review papers.
• A willingness to contribute to a collaborative and supportive team culture.
• Strong English language communication skills (verbal and written).
• Ability to work both independently and as part of a team.
• The proven ability to work on multiple tasks with competing demands and deadlines.
• Ability to train and mentor students and support fellow researchers.
• Highly developed reasoning and problem-solving skills.

Desired Criteria

• PhD gained from a top 200 QS or THE ranked University
• At least 1 year of relevant experience as a Post-doctoral researcher in academia or in industry.
• High impact first-author publications in top 10% journals in the relevant field

Should you require further assistance or if you face any issue with the online application, please feel to contact the Recruitment Team ( ).

Primary Location: Abu Dhabi UAE
Job: Post Doctoral Fellow
Schedule: Regular
Shift: Standard
Job Type: Full-time

Times Higher Education