MSc

MSc Artificial Intelligence

Build trustworthy intelligent systems from research to production

Online or on campus

24 months36 creditsEnglish

Overview

Brigant's MSc Artificial Intelligence develops the mathematical, algorithmic and systems competencies required of modern AI practitioners. You will progress from probability and classical learning through deep architectures, language models, vision and sequential decision-making, finishing with MLOps evaluation and governance. Lab work uses contemporary Python toolchains and structured peer review. The capstone produces a documented AI system with performance, safety and monitoring artefacts suitable for industrial or research scrutiny — not a demo notebook alone. MSc Artificial Intelligence is taught as one award, not a list of unrelated modules. The published length is 24 months, and the credit total is 36. Students move from Mathematics & Probability for AI through Mathematics & Probability for AI; Machine Learning Theory & Practice; Programming Intelligent Systems (Python); Data Engineering for ML Pipelines; Deep Learning & Representation Learning; Natural Language Processing & Large Language Models, and finish on MSc Artificial Intelligence Capstone Project. Each course has a question, a method, and a submission. In this field, students define a measurable question, fit a method that can be checked, and report where it fails. Reading is a dataset description, a methods note, and one published evaluation. The artefact a marker expects is a project note with the question, the method, the result, and the limitation. The award is built so that a graduate can do the following in practice: Design, train and evaluate supervised, unsupervised and deep learning models Build NLP and multimodal applications using modern transformer architectures Engineer reproducible ML pipelines with evaluation, monitoring and versioning Teaching assumes the student can read a source, attempt a problem before the seminar, and revise after feedback. Attendance at live seminars is part of the design. The capstone or final course must use the methods of the earlier courses; a project that ignores them does not pass. The pages for each course name the topics that are examined. Those topics are the syllabus. A brochure line is not a substitute for them.

The curriculum aligns with the technical depth expected at leading research universities and industry AI labs, with explicit attention to the UK GDPR, EU AI Act literacy and model documentation practices (model cards, data sheets). Optional GPU compute is available for distinction-level projects. Pathway exists into the PhD Data Science for outstanding graduates.

Who This Programme Is For

Software engineers transitioning into ML engineering or applied research
Data scientists seeking formal AI systems and deep learning depth
STEM graduates building competitive credentials for AI product roles
Technical founders embedding AI responsibly into products

Faculty spotlight

Dr. Yuki Tanaka leads representation learning research; Professor James Okello supervises responsible NLP for low-resource languages.

How you can study

Recommended

Study online

100% tuition-free online

Online tuition is free. Online examinations are free. There is no exam fee for online study.

✦ Tuition: £0 / $0
✦ Examinations are free. There is no exam fee.
✦ Learn from anywhere · Live online seminars

Apply online →

On-campus study

Study at our campus

Prefer to learn at our Edinburgh campus? Campus tuition and campus examinations are charged. The published campus price is on each programme page.

✦ Tuition: £9,500 / US$12,000/year
✦ Location: 3 Lady Lawson St, Edinburgh EH3 9DS, United Kingdom
✦ Campus examinations are charged
✦ In-person seminars, library & student services

Apply for campus →

What You Will Learn

Design, train and evaluate supervised, unsupervised and deep learning models
Build NLP and multimodal applications using modern transformer architectures
Engineer reproducible ML pipelines with evaluation, monitoring and versioning
Assess AI systems against fairness, safety and regulatory expectations
Communicate technical trade-offs to product and executive stakeholders

Curriculum

Term 1 — Foundations of Intelligent Systems

Code	Course	Credits · hours
AI501	Mathematics & Probability for AI Mathematics & Probability for AI (AI501) is a 3-credit course on MSc Artificial Intelligence, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Mathematics & Probability for AI as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. AI501-M1 · The question, the unit of analysis, and data quality — Mathematics & Probability for AI AI501-M2 · Features, baselines, and error — Mathematics & Probability for AI AI501-M3 · Validation, leakage, and what the model must not decide — Mathematics & Probability for AI AI501-M4 · Monitoring, drift, and human review — Mathematics & Probability for AI AI501-M5 · Interpretation for a non-technical decision maker — Mathematics & Probability for AI AI501-M6 · A supervised project with method, result, and limitation — Mathematics & Probability for AI	3 · 30 h
AI502	Machine Learning Theory & Practice Machine Learning Theory & Practice (AI502) is a 3-credit course on MSc Artificial Intelligence, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Machine Learning Theory & Practice as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. AI502-M1 · The question, the unit of analysis, and data quality — Machine Learning Theory & Practice AI502-M2 · Features, baselines, and error — Machine Learning Theory & Practice AI502-M3 · Validation, leakage, and what the model must not decide — Machine Learning Theory & Practice AI502-M4 · Monitoring, drift, and human review — Machine Learning Theory & Practice AI502-M5 · Interpretation for a non-technical decision maker — Machine Learning Theory & Practice AI502-M6 · A supervised project with method, result, and limitation — Machine Learning Theory & Practice	3 · 30 h
AI503	Programming Intelligent Systems (Python) Programming Intelligent Systems (Python) (AI503) is a 3-credit course on MSc Artificial Intelligence, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Programming Intelligent Systems (Python) as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. AI503-M1 · The question, the unit of analysis, and data quality — Programming Intelligent Systems (Python) AI503-M2 · Features, baselines, and error — Programming Intelligent Systems (Python) AI503-M3 · Validation, leakage, and what the model must not decide — Programming Intelligent Systems (Python) AI503-M4 · Monitoring, drift, and human review — Programming Intelligent Systems (Python) AI503-M5 · Interpretation for a non-technical decision maker — Programming Intelligent Systems (Python) AI503-M6 · A supervised project with method, result, and limitation — Programming Intelligent Systems (Python)	3 · 30 h
AI504	Data Engineering for ML Pipelines Data Engineering for ML Pipelines (AI504) is a 3-credit course on MSc Artificial Intelligence, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Data Engineering for ML Pipelines as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. AI504-M1 · The question, the unit of analysis, and data quality — Data Engineering for ML Pipelines AI504-M2 · Features, baselines, and error — Data Engineering for ML Pipelines AI504-M3 · Validation, leakage, and what the model must not decide — Data Engineering for ML Pipelines AI504-M4 · Monitoring, drift, and human review — Data Engineering for ML Pipelines AI504-M5 · Interpretation for a non-technical decision maker — Data Engineering for ML Pipelines AI504-M6 · A supervised project with method, result, and limitation — Data Engineering for ML Pipelines	3 · 30 h

Term 2 — Advanced AI Methods

Code	Course	Credits · hours
AI505	Deep Learning & Representation Learning Deep Learning & Representation Learning (AI505) is a 3-credit course on MSc Artificial Intelligence, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Deep Learning & Representation Learning as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. AI505-M1 · The question, the unit of analysis, and data quality — Deep Learning & Representation Learning AI505-M2 · Features, baselines, and error — Deep Learning & Representation Learning AI505-M3 · Validation, leakage, and what the model must not decide — Deep Learning & Representation Learning AI505-M4 · Monitoring, drift, and human review — Deep Learning & Representation Learning AI505-M5 · Interpretation for a non-technical decision maker — Deep Learning & Representation Learning AI505-M6 · A supervised project with method, result, and limitation — Deep Learning & Representation Learning	3 · 30 h
AI506	Natural Language Processing & Large Language Models Natural Language Processing & Large Language Models (AI506) is a 3-credit course on MSc Artificial Intelligence, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Natural Language Processing & Large Language Models as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. AI506-M1 · The question, the unit of analysis, and data quality — Natural Language Processing & Large Language Models AI506-M2 · Features, baselines, and error — Natural Language Processing & Large Language Models AI506-M3 · Validation, leakage, and what the model must not decide — Natural Language Processing & Large Language Models AI506-M4 · Monitoring, drift, and human review — Natural Language Processing & Large Language Models AI506-M5 · Interpretation for a non-technical decision maker — Natural Language Processing & Large Language Models AI506-M6 · A supervised project with method, result, and limitation — Natural Language Processing & Large Language Models	3 · 30 h
AI507	Computer Vision & Multimodal AI Computer Vision & Multimodal AI (AI507) is a 3-credit course on MSc Artificial Intelligence, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Computer Vision & Multimodal AI as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. AI507-M1 · The question, the unit of analysis, and data quality — Computer Vision & Multimodal AI AI507-M2 · Features, baselines, and error — Computer Vision & Multimodal AI AI507-M3 · Validation, leakage, and what the model must not decide — Computer Vision & Multimodal AI AI507-M4 · Monitoring, drift, and human review — Computer Vision & Multimodal AI AI507-M5 · Interpretation for a non-technical decision maker — Computer Vision & Multimodal AI AI507-M6 · A supervised project with method, result, and limitation — Computer Vision & Multimodal AI	3 · 30 h
AI508	Reinforcement Learning & Sequential Decision Making Reinforcement Learning & Sequential Decision Making (AI508) is a 3-credit course on MSc Artificial Intelligence, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Reinforcement Learning & Sequential Decision Making as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. AI508-M1 · The question, the unit of analysis, and data quality — Reinforcement Learning & Sequential Decision Making AI508-M2 · Features, baselines, and error — Reinforcement Learning & Sequential Decision Making AI508-M3 · Validation, leakage, and what the model must not decide — Reinforcement Learning & Sequential Decision Making AI508-M4 · Monitoring, drift, and human review — Reinforcement Learning & Sequential Decision Making AI508-M5 · Interpretation for a non-technical decision maker — Reinforcement Learning & Sequential Decision Making AI508-M6 · A supervised project with method, result, and limitation — Reinforcement Learning & Sequential Decision Making	3 · 30 h

Term 3 — Systems, Governance & Capstone

Code	Course	Credits · hours
AI601	MLOps, Evaluation & Production AI Systems MLOps, Evaluation & Production AI Systems (AI601) is a 3-credit course on MSc Artificial Intelligence, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using MLOps, Evaluation & Production AI Systems as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. AI601-M1 · The question, the unit of analysis, and data quality — MLOps, Evaluation & Production AI Systems AI601-M2 · Features, baselines, and error — MLOps, Evaluation & Production AI Systems AI601-M3 · Validation, leakage, and what the model must not decide — MLOps, Evaluation & Production AI Systems AI601-M4 · Monitoring, drift, and human review — MLOps, Evaluation & Production AI Systems AI601-M5 · Interpretation for a non-technical decision maker — MLOps, Evaluation & Production AI Systems AI601-M6 · A supervised project with method, result, and limitation — MLOps, Evaluation & Production AI Systems	3 · 30 h
AI602	Responsible AI, Safety & Governance Responsible AI, Safety & Governance (AI602) is a 3-credit course on MSc Artificial Intelligence, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Responsible AI, Safety & Governance as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. AI602-M1 · The question, the unit of analysis, and data quality — Responsible AI, Safety & Governance AI602-M2 · Features, baselines, and error — Responsible AI, Safety & Governance AI602-M3 · Validation, leakage, and what the model must not decide — Responsible AI, Safety & Governance AI602-M4 · Monitoring, drift, and human review — Responsible AI, Safety & Governance AI602-M5 · Interpretation for a non-technical decision maker — Responsible AI, Safety & Governance AI602-M6 · A supervised project with method, result, and limitation — Responsible AI, Safety & Governance	3 · 30 h
AI603	MSc Artificial Intelligence Capstone Project MSc Artificial Intelligence Capstone Project (AI603) is a 6-credit course on MSc Artificial Intelligence, with 60 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using MSc Artificial Intelligence Capstone Project as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. AI603-M1 · The question, the unit of analysis, and data quality — MSc Artificial Intelligence Capstone Project AI603-M2 · Features, baselines, and error — MSc Artificial Intelligence Capstone Project AI603-M3 · Validation, leakage, and what the model must not decide — MSc Artificial Intelligence Capstone Project AI603-M4 · Monitoring, drift, and human review — MSc Artificial Intelligence Capstone Project AI603-M5 · Interpretation for a non-technical decision maker — MSc Artificial Intelligence Capstone Project AI603-M6 · A supervised project with method, result, and limitation — MSc Artificial Intelligence Capstone Project	6 · 60 h

Code

Course

Credits · hours

AI601

MLOps, Evaluation & Production AI Systems

MLOps, Evaluation & Production AI Systems (AI601) is a 3-credit course on MSc Artificial Intelligence, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using MLOps, Evaluation & Production AI Systems as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

AI601-M1 · The question, the unit of analysis, and data quality — MLOps, Evaluation & Production AI Systems
AI601-M2 · Features, baselines, and error — MLOps, Evaluation & Production AI Systems
AI601-M3 · Validation, leakage, and what the model must not decide — MLOps, Evaluation & Production AI Systems
AI601-M4 · Monitoring, drift, and human review — MLOps, Evaluation & Production AI Systems
AI601-M5 · Interpretation for a non-technical decision maker — MLOps, Evaluation & Production AI Systems
AI601-M6 · A supervised project with method, result, and limitation — MLOps, Evaluation & Production AI Systems

3 · 30 h

AI602

Responsible AI, Safety & Governance

Responsible AI, Safety & Governance (AI602) is a 3-credit course on MSc Artificial Intelligence, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Responsible AI, Safety & Governance as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

AI602-M1 · The question, the unit of analysis, and data quality — Responsible AI, Safety & Governance
AI602-M2 · Features, baselines, and error — Responsible AI, Safety & Governance
AI602-M3 · Validation, leakage, and what the model must not decide — Responsible AI, Safety & Governance
AI602-M4 · Monitoring, drift, and human review — Responsible AI, Safety & Governance
AI602-M5 · Interpretation for a non-technical decision maker — Responsible AI, Safety & Governance
AI602-M6 · A supervised project with method, result, and limitation — Responsible AI, Safety & Governance

3 · 30 h

AI603

MSc Artificial Intelligence Capstone Project

MSc Artificial Intelligence Capstone Project (AI603) is a 6-credit course on MSc Artificial Intelligence, with 60 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using MSc Artificial Intelligence Capstone Project as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

AI603-M1 · The question, the unit of analysis, and data quality — MSc Artificial Intelligence Capstone Project
AI603-M2 · Features, baselines, and error — MSc Artificial Intelligence Capstone Project
AI603-M3 · Validation, leakage, and what the model must not decide — MSc Artificial Intelligence Capstone Project
AI603-M4 · Monitoring, drift, and human review — MSc Artificial Intelligence Capstone Project
AI603-M5 · Interpretation for a non-technical decision maker — MSc Artificial Intelligence Capstone Project
AI603-M6 · A supervised project with method, result, and limitation — MSc Artificial Intelligence Capstone Project

6 · 60 h

Career Paths

Machine Learning Engineer
Applied AI Scientist
NLP / Computer Vision Engineer
MLOps Engineer
AI Product Technical Lead

Programme Highlights

Full stack from learning theory to production MLOps
Dedicated Responsible AI module with safety case methods
Capstone with model cards and monitoring plans
Pathway to PhD Data Science for distinction graduates

Assessment

Modules are assessed through a published mix of coursework, applied projects, and examinations. Exam windows are announced in advance so students in other time zones are not forced into overnight sittings. Alternative arrangements are available where documented.

Admission requirements

Academic entry

Bachelor's degree from an accredited institution (minimum 2:2 or GPA 2.5/4.0)
Strong quantitative background (mathematics, computer science, engineering, physics or related)

English language

English proficiency: IELTS 5.5, TOEFL 46, or equivalent (waived for prior English-medium study)

Documents

Statement of Purpose (500 words minimum) outlining your goals and fit for the programme
Two academic or professional references with contact details
CV or résumé documenting relevant education and professional experience

Programme questions

How long is the MSc Artificial Intelligence?

The published duration is 24 months. Teaching language: English. Actual time-to-complete depends on mode and any recognised prior learning.

Can I study this from my country?

You may study this award fully online from your country, or — where published — on campus at Brigant. Online study does not require a student visa. Campus study may.

What does it cost?

Degree tuition for this award is published as £0 / tuition-free on the online pathway. Examination or administrative fees may apply at checkout — never an annual tuition invoice. Check the Fees page for any extras.

What are the entry requirements?

Requirements are grouped on this page (academic, English, documents). Equivalent qualifications are considered. English may be waived after prior English-medium study.

How is the programme assessed?

Assessment is typically a mix of coursework, projects, and examinations. Doctoral awards include a thesis or dissertation and an oral examination. Details sit in the programme specification and module outlines.

Will this award be recognised where I live?

Recognition of the award for local employment, professional licence, or ministry attestation is decided by your employer or regulator. University of Brigant publishes verification pages for certificates. We do not claim automatic equivalence in every country.

How do I apply?

Start an application on this website. Progress is saved from the first step. Admissions: admissions@brigant.uk.

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