Knowledge Transfer Associate: Ace Aquatec

Location: Stirling Campus

Contract type: Fixed Term for 36 months

Working pattern: Full time (35 hours per week)

The Post

Knowledge Transfer Partnerships

Ace Aquatec and University of Stirling

The Faculty of Natural Sciences is seeking a Knowledge Transfer Associate to work in conjunction with industry partner Ace Aquatec.

Ace Aquatec are based in Dundee and currently have a blended working policy where time is divided between the Dundee office, home, and roam. This role provides a unique opportunity for career development. The post-holder will receive personalised support through KTP mentoring and coaching, and have access to a wide range of training and development modules to enhance your skills.

Benefits include:

• Training budget with 10% of your working time dedicated to personal development.
• A unique and challenging career opportunity, working with both industry and academia.
• The successful applicant will develop a wide range of industrial and research skills for their future career, utilising the skills and knowledge of company supervisors, as well as benefitting from continuous academic support.
• The opportunity to manage and lead on a project early on in your career.
• Specific KTP residential training.

With agreement from the KTP team, there may be an opportunity for the Associate to undertake further qualifications at no or reduced cost under their employment by Stirling University.

Description of Duties

This role offers a unique opportunity to work at the intersection of data science, remote sensing, and environmental management, contributing to the sustainability and success of the aquaculture industry. The primary objective is to develop an advanced forecasting tool that predicts harmful algae blooms and changes in water quality, both of which pose significant risks to fish farms. By integrating high-resolution satellite data with on-site water testing, this tool will provide fish farmers with a real-time warning system, helping them to minimize risk and optimize the locations for fish growth.

The role presents a unique technical challenge, as the Associate will be responsible for identifying and interpreting satellite sensor data from several space missions and combining this with ground truth measurements to validate and optimise prediction models. These data will then be integrated into our existing customer portal, creating a user-friendly system that offers live updates on algae bloom risks. Managing this complex data integration process while ensuring scientific accuracy and practical usability will be crucial.

Essential Criteria

• MSc (preferably PhD) in data science, Artificial Intelligence, remote sensing, or closely-related fields
• Developing/applying data science techniques (big data, forecasting algorithms, deep learning, and agent-based models) to real-world problem
• Experience in data analytics/management. Demonstrable proficiency in programming in one or more languages like Python, Java, C/C++ or Matlab and an ability to self-learn new technical skills
• Experience in the use of AI/ML approaches

For further information, including a full description of duties, essential criteria and details on how to apply, please see Vacancy details | University of Stirling

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