Medical technologies include products and services used to improve patients’ health, including medical devises, in vitro diagnostics and digital health. Such technologies are used to prevent, diagnose, treat and monitor conditions, as well as manage and support a healthier lifestyle. Although medical technologies can have a huge positive impact on a patient’s health, their incorrect operation may result in harmful consequences contributing or directly causing a bad diagnosis, bad recommendation, or offering wrong information to healthcare professionals.
The regulatory environment has recognized this, and there are regulations that require devices that may result in harm to undergo certification. The regulations lay out a set of requirements that devices will have to comply with. Ultimately complying with these requirements will result in evidence and justification that will provide sufficient assurance that the devices are acceptably safe. Traditionally, assurance of devices, has relied on predictability as well as a determinism of outcomes. In other words, that we can predict what may go wrong, and given a certain set of conditions, the technology will consistently behave in the same manner. However, new technologies challenge this principle, as they utilize data- driven innovations such as artificial intelligence, which are more obscure in their operation, and thus rendering traditional safety assurance methodologies ineffective.  LSEA aims to address this limitation by developing a safety assurance methodology and toolkit for software-intensive, AI-based medical technologies. A key component of the envisaged solution will include techniques and tools for generating, combining, and evaluating synthetic datasets, that can be used for assessing safety before the formal, established certification process, reducing the risk for developers and waste for regulatory bodies.To support this vision, LSEA offers a full-time research engineer position in the cross-cutting fields of model-based engineering, data engineering, and machine learning. The research engineer will be required to undertake some or all of the duties below:

·        To conduct research and to contribute to the production of research results in the areas of model-based engineering, data engineering, and machine learning.
·        To conduct individual and collaborative research including: development of technologies; analysis and interpretation of research data; use of appropriate research techniques and methods; writing up of research results and dissemination through publications, seminar and conference presentations and public engagement and outreach activities; contributing to the identification of possible new areas of research.
·        To undertake appropriate organisational and administrative activities connected to the research project.
·        To develop and initiate collaborative working internally and externally, duties to include: the building of internal contacts

Successful candidates must hold a Master’s, or a PhD (or be near completion) in systems or software engineering, model-based engineering, safety engineering, computer science or a closely related topic. We are mainly looking for highly motivated applicants with:

·    Expertise in the principles, tools, and processes for model-based engineering.

·    Background in data engineering.  

·    Background in safety assurance will be considered an advantage.

·    Self-learning and teamwork skills.

·    Strong technical programming skills. Practical experience with Eclipse IDE, Java, and Pyhton.