Training programme

Training is designed to lead students through a programme that will progressively expand their level of expertise, in research and professionally.

A range of core modules will be taken by students. The modules will be delivered by academics in the Department of Computer Science and complemented by teaching from other departments including the School of Maths and Statistics (SoMAS).

A training needs analysis will be undertaken by all students when they first arrive to help identify which programme of modules they should take. This is run by the University of Sheffield Doctoral Development Programme.

An overview of the programme can be found below. For more detailed information, download our brochure.

Training

The CDT training programme is organised into training years, where each year contains a range of specific activities.

Year 1
Skill Foundation

Year 1
Skill Foundation

SLT is exceptional in the range of disciplines which it draws upon, from linguistics and phonetics through mathematics and computer science to signal processing and electrical engineering. The first year is therefore designed to ensure that the group of students enrolling from diverse academic backgrounds can develop into a well-integrated, self-supporting cohort. In particular, the programme launches with a three-day intensive workshop that will provide students with a shared understanding of the ethos of the CDT and an appreciation of the broader SLT research landscape. Students receive unconscious bias and ED&I training to perpetuate an environment of fairness, equality, diversity and respect. Training in the first semester will then be devoted to bringing students up to a similar skill level across a range of foundational topics. After this induction phase student PhD projects will be defined in discussion with the students, and supervisors assigned. Students start work on their research topic while still receiving foundational training.

Year 2
Scientific Foundation

Year 2
Scientific Foundation

The second year is devoted to developing advanced SLT research skills in practice, to perform the first foundational experiments and to formulate the plan for the PhD. Students are expected to submit a PhD transfer report to a confirmation panel which will assess the quality of the research and the suitability and viability of the research plan for successful PhD study. The student will engage in further cohort and external activities as well as receive further training in all training domains and modes.

Year 3
Research

Year 3
Research

This is expected to be the most productive research year. Activities will be similar to those conducted in Y2, however the students are expected to perform more leadership roles in cohort and team work, e.g., by supervising mini-projects, by stepping into planning roles in the SLT Hub or SLT Challenge activities, or by mentoring of peers. Internships are likely to happen in Y2 or Y3.

Year 4
Consolidation, Presentation and Dissemination

Year 4
Consolidation, Presentation and Dissemination

In the final year the emphasis will be on thesis completion and on ensuring impact through presentation or realisation in practical settings. Examples are writing and presentation, responsibility assessment and ethical re-evaluation, proposal writing or entrepreneurial activities. The year will end first with the completion of the required credits to receive the PGDip, followed by submission and assessment of the PhD thesis.

Course Overview

The programme requires the completion of 120 credits of modules over your four year course. In your first year, you will study 75 credits – two 15 credit core modules, plus three 15 credit optional modules. In your second, third and fourth years you will study one 15 credit core module per year. For details of the core and optional modules, please see below

Core Modules

You will complete the following modules:
Year 1

Introduction to Collaborative Research Practice for SLT

Introduction to Responsible SLT Leadership

Year 2

SLT Research and Leadership Practice 1: Scientific Foundation

Year 3

SLT Research and Leadership Practice 2: Core Research

Year 4

SLT Research and Leadership Practice 3: Dissemination and Impact

First Year Optional Modules

You will pick three from the following modules:
Scalable Machine Learning

This module will focus on technologies and algorithms that can be applied to data at a very large scale (e.g. population level). From a theoretical perspective it will focus on parallelization of algorithms and algorithmic approaches such as stochastic gradient descent. There will also be a significant practical element to the module that will focus on approaches to deploying scalable ML in practice such as SPARK, FLINK, programming languages such as Scala and deployment on elastic computing structures, cloud computing and/or GPUs.

Text Processing

This module introduces fundamental concepts and ideas in natural language text processing, covers techniques for handling text corpora, and examines representative systems that require the automated processing of large volumes of text. The course focuses on modern quantitative techniques for text analysis and explores important models for representing and acquiring information from texts.

Speech Processing

This module aims to demonstrate why computer speech processing is an important and difficult problem, to investigate the representation of speech in the articulatory, acoustic and auditory domains, and to illustrate computational approaches to speech parameter extraction. It examines both the production and perception of speech, taking a multi-disciplinary approach (drawing on linguistics, phonetics, psychoacoustics, etc.). It introduces sufficient digital signal processing (linear systems theory, Fourier transforms) to motivate speech parameter extraction techniques (e.g. pitch and formant tracking).

Machine Learning and Adaptive Intelligence

The module is about core technologies underpinning modern artificial intelligence. The module will introduce statistical machine learning and probabilistic modelling and their application to describing real world phenomena. The module will give students a grounding in modern state of the art algorithms that allow modern computer systems to learn from data.

Speech Technology

This module introduces the principles of the emergent field of speech technology, studies typical applications of these principles and assesses the state of the art in this area. Students will learn the prevailing techniques of automatic speech recognition (based on statistical modelling); will see how speech synthesis and text-to-speech methods are deployed in spoken language systems; and will discuss the current limitations of such devices. The module will include project work involving the implementation and assessment of a speech technology device.

Natural Language Processing

This module provides an introduction to the field of computer processing of written natural language, known as Natural Language Processing (NLP). We will cover standard theories, models and algorithms, discussing competing solutions to problems, describing example systems and applications, and highlighting areas of open research

The content of our courses is reviewed annually to make sure it’s up-to-date and relevant. Individual modules are occasionally updated or withdrawn. This is in response to discoveries through our world-leading research; funding changes; professional accreditation requirements; student or employer feedback; outcomes of reviews; and variations in staff or student numbers.In the event of any change we’ll consult and inform students in good time and take reasonable steps to minimise disruption

Entry and Applying

Entry requirements

Applicants should have, or be expecting to obtain, a minimum of a 2:1 undergraduate degree or masters (ideally distinction) degree in a relevant discipline. Suitable backgrounds are (but not limited to):

  • Computer Science
  • Engineering
  • Linguistics
  • Mathematics
  • Physics
  • Psychology

Regardless of background, you must be able to demonstrate mathematical aptitude (minimally to A-Level standard or equivalent) and experience of programming.

We will also consider applicants with a professional background, so long as they are able to provide evidence of demonstrable academic skills as well as practical experience.

We particularly encourage applications from women, minority groups and members of other groups that are underrepresented in technology.

If English is not your first language, you will need to meet our English Language Requirements. We ask for IELTS 7.5 overall, with no less than 7.0 in each component. Equivalent scores in other other English language qualifications are welcome; see the University’s guidance for more information on permitted qualifications.

How to apply

Applications for Cohort 2, starting in September 2020, are now open. The deadline for applications is 31st January 2020. Approximately 13 studentships are available.

You can apply through the University of Sheffield’s Postgraduate Online Application Form. Please see our Application Instructions for guidance through the application process.

Applications will be reviewed within 4 weeks of this deadline and successful applicants will be invited to interview. Interviews will be held in Sheffield.

Should there still be places available, applications will be re-opened with a second deadline in May 2020. Applicants who do not meet the eligibility requirements (please see below for details) will have their applications assessed after the second deadline.

If you have any questions about applying please email us at:

sltcdt-enquiries@sheffield.ac.uk

Please note we will retain your email address for the purpose of communicating with you about applying to study at the CDT only. Your contact details will not be used for any other topic, nor passed on to anybody else.

Eligibility

CDT studentships

CDT studentships fund four years of study, covering the annual university fees, and providing a highly competitive (enhanced) stipend. To be considered for one of the studentships, candidates will need to satisfy the EPSRC funding eligibility criteria. In particular, to be eligible for a full award (stipend and fees), a student must have:

  • settled status in the UK, meaning they have no restrictions on how long they can stay,
  • been ‘ordinarily resident’ in the UK for three years prior to the start of the grant. This means they must have been normally residing in the UK (apart from temporary or occasional absences); and,
  • not been residing in the UK wholly or mainly for the purpose of full-time education (this does not apply to UK or EU nationals).