Module finder

The aim of this module is to provide an overview of a range of microscale and nanoscale systems and devices, including sensors, actuators, and transducers. The module consists of practical works on micro and nanosystems, involving construction and characterisation with a variety of methodologies, and is supported by lectures. The assessment is in the form of two courseworks.

The aim of this module is to provide an overview of a range of microscale and nanoscale systems and devices, including sensors, actuators, and transducers. The module consists of practical works on micro and nanosystems, involving construction and characterisation with a variety of methodologies, and is supported by lectures. The assessment is in the form of two courseworks.

This module will build on the theoretical foundation acquired from the pre-course basic science package and HLTH 6195 in order to prepare you to provide high quality neonatal care.

This module will build on the theoretical foundation acquired from the pre-course basic science package and HLTH 6195 in order to prepare you to provide high quality neonatal care.

This module will provide Master’s year (level7) Neuroscience students a course based around UoS expertise in Neuroscience. This will be a research led education in which core concepts and techniques developed at levels 4-6 are iterated to an advanced level through 8 workpackages. These work packages will be led by individual (or groups of) academics around a generic structure encompassing pre-contact preparatory work and face to face contact in workshops. Within each work package the students will be provided with detailed information about an area of research and the techniques involved. Where possible the students will be given the opportunity to directly observe experimentation. Wider concepts as presented in publication formats including primary papers, reviews, and wider policy documents will be used as an important route to develop advanced understanding. The course will develop the student’s ability to understand neuroscience methodologies and synthesize material at an advanced level, consistent with a student studying at level 7.

This module will provide Master’s year (level7) Neuroscience students a course based around UoS expertise in Neuroscience. This will be a research led education in which core concepts and techniques developed at levels 4-6 are iterated to an advanced level through 8 workpackages. These work packages will be led by individual (or groups of) academics around a generic structure encompassing pre-contact preparatory work, face to face contact in workshops, post contact assessment exercise followed by a final feedback session. Within each work package the students will be provided with detailed information about an area of research and the techniques involved. Where possible the students will be given the opportunity to directly observe experimentation and undertake exercise designed to train them in appropriate analysis/modelling and data presentation. These assessments will build and use aspects of the workshop and post contact set-tasks to develop skills required to extract and critically think about data and verbally and textually discuss with advanced precision. Wider concepts as presented in publication formats including primary papers, reviews, and wider policy documents will be used as an important route to develop advanced understanding. There will be an assessment for each work package (75% of module mark) and these associated in-course assessments will be supplemented by a final exam (25% of module mark) designed to test wider comprehension of advanced neuroscience. This will take the form of a 3 hour exam in which the students will be asked to generate a short summary capsule based on their comprehension of a research paper provided at the exam session. This capsule should report on the works Background, Results, Conclusion and Significance, and include a summarising diagram. The course will develop the student’s ability to understand neuroscience methodologies and synthesize material at an advanced level, consistent with a student studying at level 7.

The module introduces more advanced operational research (OR) techniques that can be used to solve a wide range of problems in business and management including scheduling, networks, inventory control and queueing theory. It is split into two parts covering stochastic OR and deterministic OR respectively.

The Stochastic OR Techniques part introduces the concepts and applications of queuing theory and inventory control. Queueing theory can be applied to a wide range of stochastic systems, allowing estimation of statistics of interest such as resource utilisation, delays and the expected time spent within the system. Inventory control helps solve problems in inventory management where demand can be stochastic.

In the deterministic OR section, the module introduces dynamic programming, machine scheduling, project networks, and heuristics. Dynamic programming is introduced as a technique for tackling problems in which decisions can be made sequentially. For machine scheduling, the main focus is on introducing the main problem types and developing solution procedures for selected models. For project networks, the representation of projects as networks and methods for analysing such networks is covered. Following a discussion of the reasons for using heuristic methods for complex problems, a discussion of the properties of good heuristics is given. Some of the design principles for heuristics are explained, and local search heuristics are discussed.

The module introduces more advanced operational research (OR) techniques that can be used to solve a wide range of problems in business and management including scheduling, networks, inventory control and queueing theory. It is split into two parts covering stochastic OR and deterministic OR respectively.

The Stochastic OR Techniques part introduces the concepts and applications of queuing theory and inventory control. Queueing theory can be applied to a wide range of stochastic systems, allowing estimation of statistics of interest such as resource utilisation, delays and the expected time spent within the system. Inventory control helps solve problems in inventory management where demand can be stochastic.

In the deterministic OR section, the module introduces dynamic programming, machine scheduling, project networks, and heuristics. Dynamic programming is introduced as a technique for tackling problems in which decisions can be made sequentially. For machine scheduling, the main focus is on introducing the main problem types and developing solution procedures for selected models. For project networks, the representation of projects as networks and methods for analysing such networks is covered. Following a discussion of the reasons for using heuristic methods for complex problems, a discussion of the properties of good heuristics is given. Some of the design principles for heuristics are explained, and local search heuristics are discussed.