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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 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 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.

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.

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.