KTP Associate

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KTP Associate - Data Scientist
Queens University Belfast - School of Electronics, Electrical Engineering & Computer Science (EEECS)
Duration: Fixed Term - 30 months
Hours: Full-time
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.
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 enthusiastic 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.
For further information about the role including the essential and desirable criteria please visit ****  or click to Apply.
Please note the above are not an exhaustive list.
Key Dates
Closing Date: 30/06/2023
Anticipated Interview Date: 11/07/2023
Further Information
Informal enquiries may be directed to ****
Information about the KTP Programme can be found at ****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: ****; ****and  ****Location:
Belfast
Salary:
£25,000 to £40,000
Hours:
Full Time
Contract Type:
Fixed-Term/Contract
Placed On:
5th June 2023
Closes:
30th June 2023
Job Ref:
23/111015
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