Some of the programme’s learning outcomes depend on the research topic of the programme.
Knowledge and Understanding:
Having successfully completed the taught element of this programme a student will be able to demonstrate:
L1:
A deep, advanced understanding of a range of topics relevant to the active research themes in the academic unit of mathematical sciences.
L2:
An understanding of the context of, and the relationship between, the taught component and the likely research direction.
Having successfully completed the research element of this programme a candidate will be able to demonstrate:
RL1:
a proper command of the literature, including knowledge of critical open problems, as well as facility in the use of established techniques in the fields of study;
RL2:
a systematic acquisition and understanding of a substantial body of knowledge in mathematics and/or statistics which is at the forefront of the discipline;
Teaching and Learning Methods
Acquisition of knowledge and understanding is through structured exposition based on lectures, computer workshops, private study, seminars, case studies, individual and group coursework, and individual supervision under the guidance of a first supervisor, with further contributions from other members of the supervisory team.
Assessment Methods
Modules in the taught element are assessed by a combination of unseen examinations and/or coursework. Examinations and assessment in the taught component is conducted at the end of each semester and as necessary in summer referral exams. The research component is assessed by thesis and viva as governed by the regulations for an Integrated PhD in a Named Subject. Skills and knowledge will be monitored and assessed throughout the programme under the milestone reviews conducted by the Graduate School.
Subject-Specific Intellectual Skills
Having successfully completed the taught element of this programme a student will be able to demonstrate:
T1:
Technical skills in the use of appropriate mathematical and/or statistical techniques.
T2:
Critical and analytical skills using a range of appropriate mathematical and/or statistical techniques.
Having successfully completed the research element of this programme a candidate will be able to demonstrate:
RT1:
the ability to create and interpret new knowledge in the field of mathematics and/or statistics through original research or other advanced scholarship, of a quality to satisfy peer review, extending the forefront of the discipline;
Rt2:
the general ability to conceptualize, design and implement a project for the generation of new knowledge, applications or understanding at the forefront of the mathematical sciences, and to adjust the project design in the light of unforeseen problems;
Rt3:
a detailed understanding of applicable techniques for research and advanced academic enquiry.
Teaching and Learning Methods
Acquisition of knowledge and understanding is through structured exposition based on lectures, computer workshops, private study, seminars, case studies, individual and group coursework, and individual supervision under the guidance of a first supervisor, with further contributions from other members of the supervisory team.
Assessment Methods
Modules in the taught element are assessed by a combination of unseen examinations and/or coursework. Examinations and assessment in the taught component is conducted at the end of each semester and as necessary in summer referral exams. The research component is assessed by thesis and viva as governed by the regulations for an Integrated PhD in a Named Subject. Skills and knowledge will be monitored and assessed throughout the programme under the milestone reviews conducted by the Graduate School.
Transferrable/Key Skills
Having successfully completed the taught element of this programme a student will be able to demonstrate:
K1:
the ability to communicate advanced ideas clearly in a range of formats.
K2:
the ability to use IT to support their learning and research.
Having successfully completed the research element of this programme a candidate will be able to demonstrate:
RK1:
the ability to use logical argument, deductive reasoning and analysis, abstraction and generalization to solve complex problems and to report their findings;
RK2:
the ability to identify their further training needs and to take responsibility for their own professional development.
Teaching and Learning Methods
Acquisition of knowledge and understanding is through structured exposition based on lectures, computer workshops, private study, seminars, case studies, individual and group coursework, and individual supervision under the guidance of a first supervisor, with further contributions from other members of the supervisory team.
Assessment Methods
Modules in the taught element are assessed by a combination of unseen examinations and/or coursework. Examinations and assessment in the taught component is conducted at the end of each semester and as necessary in summer referral exams. The research component is assessed by thesis and viva as governed by the regulations for an Integrated PhD in a Named Subject. Skills and knowledge will be monitored and assessed throughout the programme under the milestone reviews conducted by the Graduate School.
Having successfully completed the this programme a candidate will be able to demonstrate the skills, knowledge and competencies appropriate to the award of a PhD, as specified in the Code of Practice for Research Candidature and Supervision.
