Job description


  • Entry level
  • No Education
  • Salary to negotiate
  • Sunderland


- To develop a new, improved Early Warning Signs score for acute patients

to improve care.

- NHS Data from South Tees NHS Trust will be used in the Durham Trusted

Research Environment.

- The aim is to implement a richer new EWS score.
- AI, machine learning and deep learning will be used.

Key Responsibilities

and Accountabilities:


- Assist with the dissemination of research findings and reach-out activities

through publication and presentation.

- Develop and implement a personal research plan.
- Continually update knowledge and understanding in the field of AI, machine

learning and deep learning.

Role specific:

- Study and review available research in medical informatics
- Investigate the findings for possible publications
- Prepare journal papers summarising the research findings
- Submit the papers for publication in peer-reviewed journals (of high international
- standing)
- Understand practically and implement a richer, evidence-based EWS score.
- Determine the range of evidence based indicators that will comprise a richer,

fuller EWS score.

- Determine usefulness patterns in data to improve patient care. Use Deep

Learning to improve the predictive power of new EWS scores.

- Carry out predictive analytics in practice without fault by systems or users.
- Confirm reliable data query mechanisms to provide subsets of data for

predictive analytics.

- Liaise with Information Governance Manager and Data Protection Offices in

Sunderland and Durham to: confirm flows of data from NHS Trust to the secure

medical informatics research environment (TRE); implement collaboration

agreements; confirm reliable single and multiple users meets governance




This is a 6 months fixed term full time role.

University of Sunderland

Role Profile

Part 2

Part 2A: Essential and Desirable Criteria


Qualifications and Professional Memberships:

- BSc/MSc in Computer Science

Knowledge and Experience:

- Experience specific to the subject area.
- Breadth or depth of specialist knowledge in the discipline, as well as research

methods and techniques to work within established research programmes.

- Knowledge of and experience in AI and deep learning development.
- Ability to present data in Microsoft Office and write scientific reports.
- Ability in gathering and integrating scientific data.


Qualifications and Professional Memberships:

- A post graduate qualification in a relevant subject area relating to Computer

Science such as: AI, machine learning and deep learning.

Knowledge and Experience:

- Modern web development.
- SQL data bases.
- Data preparation.
- AI, machine learning and deep learning .
- Ability to organise and prioritise own work and organise research within the

project timetable.

- Ability to maintain detailed, accurate and up to date records.

Part 2B: Key Competencies

Competencies are

assessed at the


testing stage

Analysis and Research

- Uses visualization, data sampling, data mining, big data techniques and

relevant methodical approaches to identify possible candidates for new,

improved EWS scores.

- Use of algorithms, ensembles and pipelines to determine possible candidate


- Develops overall architecture of the new NEWS system.
- Produces reports that identify key issues and findings.



- Summarise and interpret complex, conceptual and special matters to aid

others' understanding and aimed at their needs.

- Monitors understanding of others, develops approach and takes corrective

action if required.


- Conveys information of a complex, conceptual and specialist nature using a

range of styles and media selected to meet the needs of others.

- Presents complex information in formats appropriate to non-specialists without

compromising meaning.

- Monitors the reactions of others and takes appropriate steps to remedy any


Decision Making

Independent decisions

- Considers wider impact of decisions, assesses possible outcomes and their


- Uses judgement to make decisions with limited or ambiguous data and takes

account of multiple factors.

- Distinguishes between the need to make a decision, when to defer and when

not to take a decision.

Collaborative decisions

- Helps others to explore options that initially appear to be inappropriate or

unfeasible and recognise when a decision is or is not needed.

- Enables others to contribute to decisions.
- Ensures that options are weighed, outcomes identified and chances of success


- Challenges decisions, appropriately to ensure consideration and processes are


Contribute to the decision making of others

- Anticipates and highlights issues that need to be taken into account.
- Outlines possible impacting factors, assessing their degree of infl