Offshore infrastructure forms a key part of our global communication, energy generation, material transport and environment monitoring networks. This module examines the general engineering concepts and analytical techniques that are fundamental to design, operate and decommission offshore fixed, floating and seabed infrastructure in a safe, sustainable way. This includes learning about the different types of sites, platforms, and monitoring/decommissioning requirements. The module introduces analytic and numeric methods for predicting the wind, wave and current loads on offshore structures and the engineering design of different systems to ensure their safety and performance under these expected loads. The module suits a range of career pathways linked to our marine, maritime and offshore energy industries.
This is an open, flexible independent learning module. You will be allocated an academic advisor who will be available for up to 2 contact hours. These contact hours may be face to face (in person or online), by telephone and by email depending upon need, preference and availability. You will work with your academic coach to: • Identify a health-related topic. • Apply the module learning outcomes to your topic of study. • Agree an appropriate method of assessment from a list of those deemed suitable to the nature of the topic. • Agree a learning contract which details a proposed schedule for attainment. NB Where students undertaking the MSc in Professional Practice in Health Sciences select to undertake more than one Open Learning module they must demonstrate to the Programme Lead that the topic of each subsequent open module is relevant to the overall aim or trajectory of the programme of study. Additional Open Learning m modules must not have the same module code. Thus additional modules can only be selected if they bear different credit weightings.
This is an open, flexible independent learning module. You will be allocated an academic advisor who will be available for up to 4 contact hours. These contact hours may be face to face (in person or online), by telephone and by email depending upon need, preference and availability. You will work with your academic coach to: • Identify a health-related topic. • Apply the module learning outcomes to your topic of study. • Agree an appropriate method of assessment from a list of those deemed suitable to the nature of the topic. • Agree a learning contract which details a proposed schedule for attainment. NB Where students undertaking the MSc in Professional Practice in Health Sciences select to undertake more than one Open Learning module they must demonstrate to the Programme Lead that the topic of each subsequent open module is relevant to the overall aim or trajectory of the programme of study. Additional Open Learning m modules must not have the same module code. Thus additional modules can only be selected if they bear different credit weightings.
This is an independent learning module. You will be allocated an academic advisor who will be available for up to 1 contact hour. These contact hours may be face to face (in person or online), by telephone and by email depending upon need, preference and availability. You will work with your academic coach to • Agree a learning contract which details a proposed schedule for attainment. NB Where students undertaking the MSc in Professional Practice in Health Sciences select to undertake more than one Open Learning module they must demonstrate to the Programme Lead that the topic of each subsequent open module is relevant to the overall aim or trajectory of the programme of study. Additional Open Learning m modules must not have the same module code. Thus additional modules can only be selected if they bare different credit weightings.
The Open Science in Psychology module focuses on training students in current open science practices – practices which are becoming increasingly popular in modern psychology research.
The module aims to equip you with the necessary foundations to make sense of machine learning applications which may appear as block boxes without such understandings.
This module introduces you to operatic and musical-theatrical entertainments produced in Italy, France, Spain and England between 1600 and 1750 and investigates the ways in which their multimedia nature functioned in these diverse milieu.
The module introduces the operational research approach for modelling and solving engineering and management problems.
This module, alongside a second module Operational Research and Data Science Case Study 2, will form an option in Part II of MSc Operational Research, MSc Operational Research with Finance, and MSc Data and Decision Analytics. Both modules will have 30 CATS points (15 ECTS points). The new modules together offer a more structured approach to project work, which will benefit a significant part of the student body. While the total available contact time for students is comparable to that of standard projects, it is shared rather than one-to-one. The workload for the case studies is also better spread out over the academic year, with much of the preparation taking place before the summer. This module (Case Study 1) gives MSc OR/ORF/DDA students the opportunity to conduct and gain experience of an in-depth open-ended OR/Data Science investigation. The main purpose is to develop students’ skills in: identifying, organising, and directing their own work; accessing and using relevant resources, such as library and software resources; applying their OR and/or Data Science knowledge and understanding; and communicating their work, by writing a comprehensive report on the investigation and its outcomes. Case Study 1 is a primary means by which MSc OR/ORF/DDA students demonstrate their capacity for independent learning. Students will normally undertake Case Study 1 over a six-week period in the early summer (typically, mid-June until end-July). There will normally be a choice of several topics for Case Study 1. Each case study topic will be partially structured, but open ended. Contact will be via a one-hour kickoff meeting with all students in the topic group, followed by a one-hour progress meeting with all students part way through the six-week period. Weekly office hours and computer programming support sessions will also be provided. Assessment will be by means of Case Study 1 report, of 25-30 pages in length, to be submitted at the end of the relevant six-week period. Case Study 1 will act as a stepping stone to Case Study 2.
This module, alongside a second module Operational Research and Data Science Case Study 1, will form an option in Part II of MSc Operational Research, MSc Operational Research with Finance, and MSc Data and Decision Analytics. Both modules will have 30 CATS points (15 ECTS points). The new modules together offer a more structured approach to project work, which will benefit a significant part of the student body. While the total available contact time for students is comparable to that of standard projects, it is shared rather than one-to-one. The workload for the case studies is also better spread out over the academic year, with much of the preparation taking place before the summer. This module (Case Study 2) gives MSc OR/ORF/DDA students the opportunity to conduct and gain experience of an in-depth open-ended OR/Data Science investigation. The main purpose is to develop students’ skills in: identifying, organising, and directing their own work; accessing and using relevant resources, such as library and software resources; applying their OR and/or Data Science knowledge and understanding; and communicating their work, by writing a comprehensive report on the investigation and its outcomes. Case Study 2 is a primary means by which MSc OR/ORF/DDA students demonstrate their capacity for independent learning. Students will normally undertake Case Study 2 over a six-week period in the early summer (typically, early August until mix-September). There will normally be a choice of several topics for Case Study 2. Each case study topic will be partially structured, but open ended. Contact will be via a one-hour kickoff meeting with all students in the topic group, followed by a one-hour progress meeting with all students part way through the six-week period. Weekly office hours and computer programming support sessions will also be provided. Assessment will be by means of Case Study 2 report, of 25-30 pages in length, to be submitted at the end of the relevant six-week period. Case Study 2 will act as a follow on module to Case Study 1.
Operational Research (OR) uses mathematical modelling and optimisation to solve complex, real-life problems. This module will introduce some key OR techniques that are widely used to support important decisions in a wide range of applications including transportation, healthcare, finance, manufacturing and telecommunication. The emphasis of the lectures will be on introducing the mathematics of OR techniques but with careful consideration of where and how they could be implemented in practice. Workshops are used to develop skills in using specialist computer modelling software. The module is assessed via coursework and an exam.
This module will examine the strategic importance of operations management in manufacturing and service settings in both private and public sectors. In the past, where organisations tended to be more hierarchical than today, the words, "strategy" and "operations" were almost mutually exclusive. In today's highly competitive environment, though, strategic operations capabilities must be in place in order for organisations to provide goods and services that meet and exceed customer requirements. Key issues such as cost, speed, quality, flexibility and constant innovation are all part of strategic operations.
Operations management is concerned with the management of resources for producing and delivering products or services. Case study material will be used in the module to illustrate many of the important issues faced by operations managers as well as covering the core operations management techniques.
An optical waveguide is the fundamental building block in photonics and in-depth knowledge of waveguides as a light guiding medium is vital for understanding a number of photonic devices, circuits and systems. This module will introduce the fundamentals of optical waveguides and optical fibres and present a detailed description of light propagation within them. Lumerical software package will be used to simulate different waveguide geometries and analyse modal behaviour and losses. Finally, nonlinear phenomena and their application in planar waveguides and optical fibres will be briefly introduced.
Optical sensor technology is playing an increasing role in modern-day life with a range of applications emerging in areas spanning civil engineering, defence and the life sciences. This module focuses on a key area of ORC expertise that has developed in parallel with the other application specific areas of optical communications and fibre laser technologies. This module is compulsory for students on the MSc Optical Fibre and Photonic Engineering programme, and optional for students on the MSc Optical Engineering programme. The module builds on the base-concepts of optical fibre technologies (OPTO6008) taught in Semester 1, and will teach the key concepts of distributed and point sensing systems. A substantial part of the module will focus on existing and emerging applications, and on the markets of optical sensor technologies. The skills and knowledge acquired during this module will be essential for students wishing to take a final project focusing specifically on optical sensor technologies in Semester 3.
Organisations are typically faced with many decision problems in the running of their operations and they strive to make better decisions by finding good, or ideally the best (optimal), solutions to such problems. This module is concerned with how decision problems can be formulated mathematically and solved optimally to support the decision making process in organisations. The module will introduce several optimisation techniques and illustrate the application of these techniques on problems from different types of industries. The techniques introduced in this module have a wide range of applicability on decision problems arising in, among others, resource planning, machine scheduling, business investment, transportation, logistics and production planning.
The module provides an introduction to the theory and practice of optimization techniques. It covers linear programming as well as nonlinear programming. This module is suitable to those who want to apply computational optimization methods to their problems, which can arise from a variety of applied disciplines such as compuer science and engineering.
