Research Assistant - Participatory Toolkit

The applicant will support an educational research project in the Faculty of Dentistry, Oral and Craniofacial Sciences. The research involves analysing data collected from a dashboard including teacher feedback, student feedback and self-reflections.

The role includes conducting thematic analyses and working with quantitative data. The successful candidate will be supported in developing their qualitative and quantitative research skills through guidance towards self-directed learning resources and ad-hoc training to enhance their expertise. 

The Research Assistant will contribute to generating meaningful insights to inform educational practices, particularly in assessment and feedback.

Key responsibilities:

1. Clean, process and manage datasets from multiple excel sources for analysis.
2. Provide quantitative and qualitative research support.
3. Assist in conducting thematic analyses to identify themes from qualitative data.
4. Perform quantitative analysis of student grade performance.
5. Participate in discussions, communicating and presenting research at meetings.
6. Contribute to writing reports, presentations and academic publications.

A third-year undergraduate student or above in any subject area.

Desirable skills: 

1. Knowledge of qualitative and quantitative research methods and techniques 
2. Some understanding of thematic analysis and experience in working with large datasets. 

Required skills:

1. Basic knowledge of excel.
2. Ability to work independently and collaboratively remotely.
3. Ability to manage tasks and deliver results under tight deadlines.
4. Strong work ethic, commitment to data accuracy.
5. Ability to work with limited supervision.

To provide practical and emotional support to assist a student on the autism spectrum in taught sessions (lectures, seminars and labs). To support the student with explaining tasks and providing mutual regulation strategies that will ensure widening participation and access, academic progress and student retention in compliance with the Equality Act and the mission of Student Services.

This role is 9.5 hours a week distributed as below:

• Monday 2.5 hours: 10-11am; 12-1pm (IT class); 2pm-3:30pm
• Tuesday 3 hours: 11-1pm; 4-5:30pm
• Wednesday 1.5 hours: 9- 11am
• Thursday 1.5 hours: 10-11:30am (lab)

There will be breaks in between taught sessions.

Minimum of 5 GCSEs (or equivalent) at Grade A-C including English and Mathematics - Essential
A level qualifications (or equivalent), or equivalent relevant experience - Essential
Educated to degree level in a relevant field or equivalent experience - Desirable 
Relevant further educational or professional qualifications e.g. Mental Health First Aid. - Desirable
 

Experience

• Experience of studying in Higher Education - Essential
• Knowledge of autism spectrum condition - Desirable
• Some experience/understanding of providing support for individuals on the Autism Spectrum - Desirable

Skills

• Knowledge of SCERTS/Autism Spectrum Condition - Desirable
• Mutual Regulation strategies: e.g. deep pressure techniques, grounding and breathing strategies - Desirable
• Thorough knowledge of the campus - Essential
• Awareness of disability issues ? recognising the most effective method of communicating during periods of dysregulation - Essential

Attitude

• Outgoing; great interpersonal skills - Essential
• Must be able to work calmly under pressure - Essential
• Must be able to support and manage fluctuating behaviours - Essential
• Must be willing to attend relevant training sessions arranged by the Disability and Dyslexia Service - Essential

Other - All essential

• Flexible working timetable
• This post is subject to a basic DBS check.
• The ability to meet UK ?right to work? requirements

Job Title:  Digital Fingerprinting Feature Engineering 

Nvidia CSIT Cyber-AI hub intern in feature engineering (up to a maximum of 15 hours per week for 20 weeks). 

The successful candidate will be working as an intern with the Nvidia CSIT Cyber-AI Hub project team on preparing data from multiple sources for AI training. This includes proper data storage, organization, cleaning and feature engineering/preprocessing tasks. 

The candidate will help to improve the current feature selection and engineering process for the development of the behavioral model proposed by the team. In addition, and upon obtaining successful results, the candidate is expected to assist with the integration of the software in the Nvidia Morpheus github page. 

Degree in Computer Science or in a relevant field. 

Have, or be about to obtain an artificial intelligence related postgraduate degree. 

Essential criteria: 

In depth knowledge of Artificial Intelligent/Machine Learning concepts 

Experience in data management for AI training. 

Understanding in networking and security best practices. 

Strong programming skills (Python) 

Proficient Linux/Windows skills. 

Desirable criteria: 

Experience with ML projects including feature engineering 

Understanding of cyber security concepts, e.g. Cyber Kill Chain and Defense-in-depth. 

Knowledge in the MITRE ATT&CK framework and other threat modelling tools.