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Maths Assessment Developer
Job Description
STACK is a quiz question type in KEATS which allows module leaders to ask mathematically sophisticated questions where students provide sophisticated answers. The questions are built using a computer algebra system (Maxima) which means that they can be randomised, students answers can be automatically marked, and dynamic and high-quality feedback provided.
Successful applicants will be trained in creating questions using STACK and then work with the leader(s) of a first or second year undergraduate mathematics module to develop a bank of questions for the module using STACK, supported by Dr. David Sheard. The possible modules involved are
- 4CCM141A Probability and statistics I
- 4CCM131A Introduction to dynamical systems
- 4CCM114A Linear algebra and geometry II
- 5CCM221A Real analysis
- 5CCM211A Applied differential equations
- 5CCM212A Complex analysis
- 4CCM112A Calculus II
- 4CCM111A Calculus I
- 4CCM113A Linear algebra and geometry I
Responsibilities:
Successful applicants will be required to:
- Attend training on how to author questions in STACK (included in pay).
- Meet and work with the module leader(s) of a course to develop educational resources in STACK.
- Ensure the STACK resources they create meet minimum quality standards as agreed at the start of the role.
- Work to weekly/fortnightly deadlines throughout the teaching semester.
- Seek help/guidance when needed and use online reference material as appropriate (including programming documentation and educational literature).
- Submit timesheets regularly and adhere to administrative requirements of the position.
Benefits:
Successful applicants will:
- Have the opportunity to collaborate closely with academics in the design of teaching resources.
- develop skills including programming, unit testing, and teamwork.
- develop understanding of pedagogy, especially related to assessment for learning and feedback.
These may be particularly relevant to people thinking of a future career in academia or education generally.
These positions are advertised as one-off, fixed-term positions. In total, successful applicants will be expected to work 50 hours
Please provide a short personal statement summarising which of the desirable and advantageous and skills/experience you have, how you satisfy the qualifications requirement, and which of the modules listed in the job description you would like to write STACK questions for. You may wish to upload a 1 page CV as well.
If you have any questions about the role or application process, please email David Sheard at david.1.sheard@kcl.ac.uk.
Qualifications
Applicants should be one of the following:
- Level 6 or 7 undergraduate student with a mark of 70+ in the module(s) from the list they would like to work on
- Postgraduate taught student with a mark of 70+ in a similar module to the one(s) from the list they would like to work on
- PhD student with sufficient subject knowledge to effectively GTA the module(s) from the list they would like to work on
Skills
Desirable:
- Programming experience, especially in Maxima or Mathematica
- Good organisational and teamworking skills
- Excellent academic written English
Advantageous:
- Some familiarity with editing KEATS pages
- Interest in teaching, pedagogy, or assessment design
MORE JOBS LIKE THIS
The Policy Institute is looking for a research assistant to work with Professor Duffy at the Policy Institute on a project analysing longitudinal survey datasets to study the evolution of attitudes and values for generations in the UK and worldwide. The project will involve two main tasks, which are expected to last for a total of 10 days, listed below:
Task 1: Longitudinal data cleaning and data visualisation
We want to update a PowerPoint resource pack consisting of c.100 charts using data from sources including Ipsos, Eurostat, the US General Social Survey, the British Social Attitudes, the European Social Survey and more. The research assistant will work with the Policy Institute team updating existing code (currently available in R/Stata) to extend longitudinal series, produce results breakdowns by cohorts/generations and update the charts sourcing using the most recent data points for these studies.
Task 2: Investigating the divergence between Gen Z men and women
We would like to produce a new set of c.40 charts, taken from the larger resource pack from Task 1 that split Gen Z men and women over time, on a smaller selection of questions and data sources, where sample size allows. The research assistant will work with the Policy Institute researchers to identify topics and data sources to carry out this task, and then produce a set of visualisations and statistical models to identify any statistically significant differences between Gen Z gender groups.
FURTHER INFORMATION
The post is available on a fixed term basis. The research assistant will be able to organise their working hours and patterns with flexibility, but we are aiming to complete work on both tasks by 28th February 2025.
Qualifications
Essential skills: Enrolled in a Bachelor?s degree in a relevant social science discipline (e.g. politics, economics, social sciences, geography, etc.).
Desirable: Enrolled in a postgraduate (eg. MSc/MA/PhD) degree in a relevant social science discipline (e.g. politics, economics, social sciences, geography, etc.).
Skills
Essential skills: Knowledge of quantitative research methods. Experience using statistical software including R, Stata and/or SPSS. Experience using PowerPoint. Excellent written and oral communication skills. Punctual, reliable, committed, proactive. Ability to work independently and as part of a team.
Desirable skills: Experience with the analysis of longitudinal survey data. Experience producing charts using Excel and Powerpoint. Interest in public opinion and public attitudes research. Interest in generations research.
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.
Qualifications
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
Skills
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.
Qualifications
Degree in Computer Science or in a relevant field.
Have, or be about to obtain an artificial intelligence related postgraduate degree.
Skills
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.
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