KTP Associate - Data Scientist Belfast, Northern Ireland
Queen's University Belfast
Full Time
Belfast, Northern Ireland
25000 - 40000 GBP ANNUAL Today
Job description
Job details
Job reference
23/111015
Date posted
05/06/2023
Application closing date
30/06/2023
Salary
£25,000 - £40,000 per annum. One of the key KTP benefits for graduates is access to a £9,500 training and travel budget over the 30-month project.
Job category/type
KTP
Attachments
Blank
KTP Associate - Data Scientist
Job description
The School of Electronics, Electrical Engineering & Computer Science (EEECS) at Queen’s University Belfast, is currently seeking to appoint to the post of KTP Associate – Data Scientist.
Through the Knowledge Transfer Partnership (KTP) Programme Queen’s University Belfast in partnership with Sensoteq Ltd have an exciting employment opportunity for a graduate to work on a project to develop an innovative machine learning based condition monitoring package for detection of changes, energy efficiency analysis and related faults in industrial wireless sensing.
Sensoteq operate within the “Predictive Maintenance” space, specifically providing Industrial Internet of Things (IIoT) solutions to monitor the condition of machinery. Sensoteq’ s purpose is to provide the industrial world with a more sustainable future, preventing failure and reducing energy wastage by wirelessly connecting machines to the internet and analysing the relevant data.
This is a unique opportunity for a dynamic and motivated Computer Science or Electronic Engineering graduate to work in Sensoteq Ltd (Belfast) on a 30-month collaborative project with School of Electronics, Electrical Engineering and Computer Science (EEECS) at Queen’s.
The KTP Associate will lead on the delivery of the following key project stages under the guidance of company and academic supervisors:
- Stage 1 –Exploratory data analysis
- Stage 2 –Model development and model selection
- Stage 3 –Model and Knowledge Transfer
- Stage 4 –Relevant Data Analysis
- Stage 5 –Machine Learning based model development and refinement.
- Stage 6 - Finalise Model and Knowledge Transfer
- Stage 7 –Dissemination and Exploitation
The successful candidate must have, and your application should clearly demonstrate you have:
- Hold, or be about to obtain, a PhD in a relevant discipline or 2 years of industrial work experience.
- Hold at least a 2.1 Degree (or equivalent) in Computer Science, Electronic Engineering, or a closely related discipline.
- Completion of a large relevant research project or student placement and have used machine learning techniques to develop a model with a successful outcome for the project.
- Applicants should indicate how their experience could be applied to this post.
- 2 years of academic or industrial work experience in machine learning based model development, statistical analysis, and data science*.
- Demonstrate work experience as part of a team.
- Understanding of both supervised and unsupervised machine learning methods*.
- Demonstrate an understanding of evaluating statistical models*.
- Data-science-related programming skills in Python, R, or Matlab (Python preferred).
- Experience with computer aided data analytics and visualisation framework software packages.
- may be demonstrated through a publication or a completed industrial project.
Please note the above are not an exhaustive list. For further information about the role including the essential and desirable criteria, please click on the Candidate Information link below.
Queen’s University is committed to promoting equality of opportunity to all. We have created an inclusive culture by establishing staff networks such as iRise (Black, Asian, Minority Ethnic and International Staff Network) and PRISM (LGBTQ+) which help us progress equality.
We also subscribe to Equality Charter Marks such as the Diversity Charter Mark NI in addition to Athena Swan. For further information on our commitment to Equality, Diversity and Inclusion, please visit: www.qub.ac.uk/diversity; www.qub.ac.uk/qgi and www.qub.ac.uk/sites/StaffGateway/StaffNetworks/
Informal enquiries may be directed to [email protected]
Candidate Information
About the KTP Programme
About the School
About Sensoteq
Attractive Package
Further information for international applicants
