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Research Assistant/Associate in Computer Vision and Machine Learning

Requisition ID:  5204
Location: 

Newcastle, GB

Contract Type:  Fixed Term
Working Pattern:  Full Time
Posted Date: 

 

We are a world class research-intensive university. We deliver teaching and learning of the highest quality. We play a leading role in economic, social and cultural development of the North East of England. Attracting and retaining high-calibre people is fundamental to our continued success.

 

Salary: £28,331.00 - £44,045.00 

Closing Date:  15 October 2020

 

 

The Role

 

Based in the School of Engineering this is an exciting opening in the School of Engineering for a dynamic individual to implement a programme of computer vision and machine learning research applied to heterogeneous geospatial datasets. You will work with an interdisciplinary group in the School as part of the UKRI NERC-funded PYRAMID project, bringing expertise in CV and AI for the purposes of populating a dynamic, near-real-time urban flood risk model. You will hold a PhD in a relevant subject, have strong software engineering skills and experience in computer vision, machine learning and geospatial data science. Additional skills in hydrology/hydrodynamics are desirable, but not essential.

 

You will be based on Newcastle University City Centre campus, working alongside other NERC PYRAMID researchers and having access to a wider team of experts in big data, computer vision, artificial intelligence, geospatial engineering and hydrology / hydrodynamic modelling within Newcastle University and at project partner Loughborough University.

 

You will benefit from having access to a wide range of Continuing Professional Development courses, some of which are accredited by professional organisations amongst other employee benefits. The School of Engineering has a strong record in the Athena SWAN scheme. Athena SWAN advances gender equality with representation, progression and success for all. The School of Engineering was granted a Bronze award in October 2018, valid for three years

 

The position is available on a full time, fixed term basis. Available to commence immediately and tenable for 24 months (in the first instance) or until the 13th August 2022, whichever date is soonest.

 

For all informal enquiries please contact Professor Jon Mills via email: jon.mills@newcastle.ac.uk

 

For information about the School of Engineering, please click here

 

As part of our commitment to career development for research staff, the University has developed 3 levels of research role profiles..  These profiles set out firstly the generic competences and responsibilities expected of role holders at each level and secondly the general qualifications and experiences needed for entry at a particular level.

 


Key Accountabilities

 

1. Develop and apply photogrammetric computer vision (PCV) to street-level and aerial imagery to identify flood-relevant geospatial information such as porous surfaces, gaps within structures, or sources of debris.
2. Design a framework to structure and link the flood risk components from PCV and other disparate data sources, with outputs in suitable formats for modelling.
3. Create procedures for dynamic dataset maintenance and near-real-time updates, in the form of a data pipeline.
4. Develop a demonstrator of dynamic data-model update and feedback capability using live data streams.
5. Actively disseminate research outcomes, including preparation of manuscripts for high quality journals, delivery of poster and oral presentations at international conferences.

 

 

The Person (Essential)

 

Knowledge, Skills and Experience

 

• Experience of developing, setting-up and running integrated software solutions.
• Good data analytical skills, with the ability to work methodically and accurately in designing, executing and writing up research independently;
• Experience in computer vision, machine learning and geospatial data science;
• Experience of collaborative working with academic and stakeholder partners.
• A developing track record of peer reviewed publications in internationally recognised journals and experience of presentation of work to the national/international research community(Desirable for Research Assistant);
• Knowledge of innovative ways of presenting research outputs(Desirable for Research Assistant);
• Skills / expertise in hydrology/hydrodynamics (Desirable for Research Assistant);
• Knowledge of best practice in software engineering such as version control, automated testing and continuous integration (Desirable for Research Assistant)

 

 

Attributes and Behaviour

 

• Excellent communication skills, both written and verbal; ability and willingness to communicate your research at national and international conferences, to fellow academics in other disciplines and to stakeholders;
• Excellent interpersonal skills to build trust with collaborating groups;
• The ability to work well both independently and as part of a team;
• Good planning, organisational and time management skills;
• Professional approach with a commitment to equality, diversity, dignity and respect in the workplace.

 

 

Qualifications

 

• A good Bachelor’s or Master’s degree in a relevant subject e.g. Computer Science, Civil Engineering, Geospatial Engineering, Mathematics.
• PhD awarded (or nearing completion) OR equivalent experience in computer vision, machine learning or geospatial data science or a closely related discipline.

 

Newcastle University is committed to being a fully inclusive Global University which actively recruits, supports and retains staff from all sectors of society.  We value diversity as well as celebrate, support and thrive on the contributions of all our employees and the communities they represent.  We are proud to be an equal opportunities employer and encourage applications from everybody, regardless of race, sex, ethnicity, religion, nationality, sexual orientation, age, disability, gender identity, marital status/civil partnership, pregnancy and maternity, as well as being open to flexible working practices.

 

Requisition ID: 5204