Research Assistant, Image Data Processing and Experiment Automation
The Singapore Centre for Environmental Life Sciences Engineering (SCELSE) is a unique interdisciplinary Research Centre of Excellence (RCE), funded by National Research Foundation, Singapore Ministry of Education, Nanyang Technological University and National University of Singapore. Hosted by NTU in partnership with NUS, SCELSE is linking new insights from the life sciences with expertise from the emerging technologies in engineering and natural sciences to understand, harness and control microbial biofilm communities.
Singapore Centre for Environmental Life Sciences Engineering invites applications for the position of Research Assistant.
- Developing customised optical imaging systems for biological and medical applications as well as overseeing operations of the microscopy facility at SCELSE
- Contribute to both branches of these group activities by participating in the development and implementation of hardware and software solutions for microscope control and automation as well as solutions for handling and processing large volumes of multivariate image data
- Other tasks will be centred around storage and processing of image data acquired at SCELSE microscopy facility, and troubleshooting issues related to communication between microscope hardware and software
- Contribute to the development of hardware and software solutions for microscope control and automation
- Contribute to the development of hardware and software solutions for efficient handling and processing large volumes of multivariate image data
- Implement and troubleshoot these hardware and software solutions and ensuring their continual functionality
- Solve technical problems related to computer control of microscope hardware and imaging automation
- Administration of computational infrastructure for image data storage and processing
- Perform various image data processing tasks
- Minimally Diploma in computer engineering, electronic engineering or related technical fields
- In-depth knowledge of computer hardware and software
- Knowledge of computer networks and storage pools architecture
- Knowledge of programming (C++ being preferred) and scripting (e.g. bash, python, WSH)
- Experience in scientific experiment automation or industrial automation is highly preferred
- Experience in scientific image processing and electronics design are an advantage
- Other preferred skills and experience include: data storage configuration (e.g. ZFS), GPU accelerated computing, notebook documented workflows, computer network and server administration
- Willingness to learn new skills and solve diverse experiment automation and data processing tasks in a multidisciplinary environment
We regret that only shortlisted candidates will be notified.
Hiring Institution: NTU