Teaching

The programme is hosted by the Department of Chemical Engineering and Biotechnology, and benefits from contributions by a broad range of departments across the University. It is further strengthened through close collaboration with industry partners, national research institutions, policy bodies and third-sector organisations.

The programme combines taught, research and skills training elements, running from October to August:

I. Taught Component. In the first term, students undertake three taught courses:

I.1. Principles of Sensing. This course, delivered through lectures and practical sessions, serves as the fundamental pillar of the curriculum. It introduces a range of sensor technologies, their operations and applications, and provides hands-on skills in sensor design, engineering and evaluation.

I.2. Machine Learning for Data-Intensive Science. Offered through lectures and supervised practicals, this course explores the use of artificial intelligence and machine learning in data-intensive science, covering the key frameworks and concepts required for understanding and applying industry-standard approaches.

I.3. Responsible Research and Innovation in an Uncertain World. This course covers topics in entrepreneurship, sustainable development, responsible research and innovation, equality, diversity and inclusion, environmental sustainability and trusted research, being normally co-delivered with industrial contributors and third-sector partners.

II. Research Component. The programme’s research component encompasses two elements that allow students to experience the nature of scientific work and prepare them for further studies and careers in either academia or industry:

II.1. Research Project. Beginning in the second half of the first term and continuing through to June, the research project enables students to work on a topic selected from a diverse range of sensor-related areas. The projects are designed to challenge students and expose them to emerging technologies and applications.

II.2. Team Challenge. From early June to the end of August, students work collaboratively on a topical sensing problem as a single cohort. The team challenge may involve aspects of business, policy and public engagement. Students may, for example, be tasked with developing a technology prototype and accompanying business plan, producing a white paper in collaboration with policymakers, or engaging with the public through interactive outreach strategies.

III. Further Skills Training. Students are given opportunities for further skills training beyond the formally taught modules and research projects, which include workshops, seminars and industry lectures.

One to one supervision	The programme director(s) and manager(s) provide day-to-day supervision dealing with student queries. Students will normally meet with their research supervisor(s) for one hour per week; mentors may be provided for laboratory work, providing equipment or technique-specific assistance as required. The University of Cambridge publishes an annual Code of Practice which sets out the University’s expectations regarding supervision.
Seminars & classes	Students are expected to attend sensor-related seminars and workshops or skills sessions, approximately once per month.
Lectures	Lectures take place in Michaelmas Term (October to December): approximately 8 hours for the Principles of Sensing course, approximately 24 hours for the Machine Learning for Data Intensive Science course, and approximately 16 hours for the Responsible Research and Innovation in an Uncertain World course.
Practicals	The Principles of Sensing course involves approximately 16 hours of practical sessions in Michaelmas term.
Literature Reviews	The work for the Research Project includes a literature review.
Posters and Presentations	Students will present their results from the Research Project and the Team Challenge in the form of a report and poster and/or oral presentations.
Taught/Research Balance	Predominantly Research

Placements

Students will carry out practicals and research projects organised by the participating departments. Industrial partners might offer the opportunity for students to carry out parts of the projects in their research facilities.

Feedback

Students can expect to receive termly online progress reports, and they are asked to attend study meetings with the programme director(s)/programme manager(s) at the end of each term to discuss progress and academic-related matters. They may be provided additional feedback by the module leaders for the programme’s Taught Component. In the programme’s Research Component, students will receive continuous feedback from their project supervisor(s).

Students will have access to the programme director, the programme manager and other staff delivering the programme throughout the year, and are encouraged to provide feedback on several elements of the programme for monitoring and continuous improvement purposes.

Assessment

Thesis / Dissertation

Assessment of the Research Project will comprise a report (up to 10,000 words), a literature review (up to 4,000 words), and both poster and oral presentations.

Assessment of the Team Challenge will involve the whole cohort collaborating to produce a report of up to 20,000 words, and each student writing individual reflective reports of up to 1,000 words. Additional assessment components include an individual progress update oral presentation and a final group oral presentation.

Essays

Part of the Principles of Sensing course, part of the Machine Learning for Data Intensive Science course and the entire Responsible Research and Inclusive Innovation in an Uncertain World course are assessed via coursework. The assignments may be reports/essays, presentations, class participation, or a combination of these.

Written examination

Part of the Principles of Sensing course and part of the Machine Learning for Data Intensive Science course are assessed through written exam.