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Research Fellow / Assistant / Associate Job Description
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Sample Job Description
Research Fellow in Computer Vision and Machine Learning at University of the West of England Bristol
(Advertised during Feb 2021)
In this page details from this official job description are given below.
Research Staff - Job description
Post Title: Research Fellow in Computer Vision and Machine Learning
Grade: G
Faculty/Service: Engineering Design and Mathematics / Faculty of Environment and Technology
Accountable to: Centre Director
Accountable for:
Post no: R02121
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Job context
This is a generic job description in use in all UWE faculties. It encompasses the full range of accountabilities associated with this role. Specific requirements for the role holder will be determined as part of annual performance and development review (PDR) objective setting and work load reviews.
About us: The Centre for Machine Vision (CMV) is seeking to appoint two Post-Doctoral Research Fellows to focus on applications of computer vision and machine learning (these are full time, fixed term posts for three years). The CMV is within the Department of Engineering, Design and Mathematics and is located within the Bristol Robotics Laboratory (https://www.bristolroboticslab.com/centre-for-machine-vision), a joint venture between UWE and Bristol University. The CMV is currently comprised of 15 researchers, five PhD students and four visiting academics and attracts funding from a range of bodies that include BBSRC, EPSRC and InnovateUK. We also undertake research funded by medical charities, as well as commercial consultancy work for various companies. The CMV specialises in realworld applications of computer vision and machine learning for the realisation of working prototypes and demonstrators, with a strong emphasis on 3D data capture, modelling and analysis. Applications include agriculture (animal and plant monitoring), medicine (vision systems for patient diagnosis/analysis and human-computer interaction) and biometrics (human and animal face recognition and non-contact palmprint recognition). Many of the technologies being developed in our laboratory can help with facilitating social distancing, and so are particularly relevant to the current COVID-19 pandemic. The work of the CMV team was graded to be of international standing in the 2014 REF, with 75% of its work considered world-leading/internationally excellent in terms of originality, significance and rigour.
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Job purpose
To conduct research individually or as part of a research team in order to achieve research outcomes which meet the objectives of the project/department/faculty.
The appointed researchers will work both individually and as part of a small team initially on a number of related short-term (e.g. six months to a year) projects which may run concurrently. For the first year, he/she will focus on applications of computer vision and deep learning, in a range of applications, including agri-technology, often working closely with our existing collaborators as well as networking with new partners. There will also be opportunity for input on other projects, including human-computer integration and healthy aging, that will depend on the progress on the existing projects and the priorities associated with these projects.
The researcher will undertake project management and/or supervise multi-disciplinary teams. You will participate in advanced research in computer vision and machine learning including:
• Data augmentation
• Face imaging
• Object detection and tracking
• Real-time vision algorithms
• Deep reinforcement learning
• Deep Convolutional Neural Network
• GANs
• 3D reconstruction
The researcher will also be working closely with our external partners as well as with CMV colleagues to help develop new funded projects. Funding is available for making visits to the collaborating research groups and there will also be the opportunity to attend international conferences.
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Principal accountabilities
1. Research and scholarship
Undertakes basic research for example by preparing, setting up, conducting and recording the outcome of experiments and field work; typically this will involve the use of machine vision cameras and computers, with deep learning for data modelling and analysis.
Analyses and interprets the results of own research and generate original ideas based on outcomes.
2. Workload and project planning
Plans own day-to-day research activity within the framework of the agreed programme.
Co-ordinates own work with that of others to avoid conflict or duplication of effort. Contributes to the planning of research projects.
3. Communications
Presents information on research progress and outcomes to bodies supervising research, e.g., steering groups. This could include making presentations at conferences.
Writes up results of own research and contributes to the production of research reports and publications
4. Teamwork/people management
Provides guidance to support staff and any students who may be assisting with the research. Actively participates as a member of a research team.
5. Liaison and networking
Makes internal and external contacts to develop knowledge and understanding and to form relationships for future collaboration.
Contributes to preparing proposals and applications to external bodies e.g., in relation to funding.
6. Teaching and learning
Assists in the supervision of student projects. You will also be expected to contribute to teaching, for example, introductory courses in machine vision and deep learning, or in research methods and use of equipment / software.
7. Other
Sensory and physical demands will vary from relatively light to a high level depending on the discipline and the type of work.
Depending on the area of work and level of training received, may be expected to conduct personal risk assessments.
Comply with the University’s equal opportunities policy, and use this role to promote equal opportunity wherever possible.
Responsible for your own health and safety and that of your colleagues, in accordance with the University’s health and safety policy.
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Person specification
Qualifications/Professional membership
Essential
1. A good first degree in a relevant area.
Desirable
1. A PhD (or equivalent) in computer science or a related field, with focus on computer vision or deep learning.
Knowledge/Skills/Experience
Essential
1. Experience of machine learning, e.g. application of CNNs.
2. A solid foundation in computer vision.
Desirable
1. Experience of using computer vision for: object detection, tracking and recognition; 3D computer vision and activity recognition.
2. Software, e.g. programming in C++ or Python.
3. A proven research record and publications in the relevant areas.
4. Understands how to conduct research using appropriate research techniques and implementing new research methods. Can demonstrate a commitment to continually updating knowledge and understanding in field or specialism.
5. Possesses sufficient breadth or depth of specialist knowledge in the discipline and of research methods and techniques to work within research programmes.
6. Is skilled in effectively analysing and interpreting research data to generate outcomes.
7. Possesses effective communication skills in order to communicate research progress and outcomes, orally and in writing to colleagues and other supervisory groups, and at conferences, where appropriate.
8. Is able to create effective working relationships with colleagues and students across the team/faculty. Is also able to demonstrate an ability to build and participate in internal and external networks in order to enhance individual and UWE profile.
9. Can effectively manage own workload, research resources and administrative activities.
10. Has the ability to support students and their projects. Can also demonstrate the ability to deliver introductory courses on research methods and equipment.
11. Experience of working with hardware, instrumentation and data gathering.
12. Experience with hyperspectral imaging and UAV-based imaging is a strong plus.
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Special conditions
xxxxxxxxxx
Health and Safety/Risks
This post has been identified with the following risks: (activities, hazards or exposures)
Risk 1: DSE / VDU User
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