Job description

Requirements

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

Description

- 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:

Generic:


- 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


standards.


Special


Circumstances:

This is a 6 months fixed term full time role.


University of Sunderland


Role Profile


Part 2


Part 2A: Essential and Desirable Criteria


Essential


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.

Desirable


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


interview/selection


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

algorithms


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

Communication


Oral


- 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.


Written


- 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

miscommunications.


Decision Making


Independent decisions


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

likelihood.


- 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

considered.


- Challenges decisions, appropriately to ensure consideration and processes are

robust.


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