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This is a list of industrial projects offered through the Cambridge Mathematics Placements programme for summer 2019 and will be updated on an ongoing basis. Projects should be aimed at students working in the Faculty of Mathematics. To remove a listing, please contact Jacob Rasmussen.

Understanding cut rose performance through long term quality monitoring and analysis

Contact Name Richard Boyle
Contact Email
Company Name MM Flowers Ltd
Address Pierson Road The Enterprise Campus, Alconbury Weald, PE28 4YA
Period of the Project 8 weeks
Project Open to Undergraduates, Part III, PhD students
Deadline to Register Interest  
Brief Description of the Project MM Flowers, established 11 years ago, is the UK's leading, integrated cut flower supplier, with a unique ownership model and innovative practices. MM Flowers is owned by the Munoz Group, a leading breeder, grower and distributor of citrus and grapes; Vegpro, East Africa's largest flower and vegetable producer; and Elite, the leading flower grower and breeder in South America. MM Flowers supplies many of the major high street retail brands, whether in store estate or directly to consumers. The UK cut flower industry can be challenging, where customers expect high quality flowers at competitive prices. The vast majority of species utilized are highly perishable, short life products, which are transported from many different regions around the world. Pre- and post-harvest management, logistics and environmental control are all factors that can positively or negatively impact upon flower quality. MM Flowers receives circa 400 million stems of cut flowers annually across 60 different species and 2000 individual product groups. There is a dramatic increase during periods such as Valentine's and Mother's Day. To ensure the quality of product is delivered successfully and is of the required standards for bouquet production, a dedicated quality control team undertake daily inspections of the flowers received, whilst MM has further developed its own dedicated R&D business, APEX Horticulture, to provide solutions to maintain or enhance flower quality. Through APEX, MM has established large and detailed data sets on the quality and performance of many key flowers, including roses and lilies. The rose data set alone is comprised of over 500,000 data points, from grower information through to quality and performance attributes. This data is typically used by the business in regular feedback to farms, to inform decisions on varietal selections and to provide baseline data for specific projects, for example. These data sets present an opportunity to undertake more detailed analysis of long term trends, and how various factors influence the end consumer quality and performance. In addition, there is the possibility to develop a process to allow for future data to be incorporated and analysed more efficiently, allowing for quicker and more accurate decision making. Further to this, the student can expect to gain valuable experience working within a fast-paced business in the fresh produce sector. This includes liaising with different departments, project management, communication skills, and working towards the needs of the business.
Skills Required Strong computer skills Experience with statistics and modelling Clear communicator Self motivated Demonstrates initiative Project management
Skills Desired  


Mathematical Finance in the Energy Sector

Contact Name Lee Momtahan
Contact Email
Company Name Centrica Energy Marketing and Trading
Address 2nd Floor, Park House, 116 Park Street, London W1K 6AF
Period of the Project 8 weeks between late June and 30 September
Project Open to Part III students
Deadline to Register Interest February 22
Brief Description of the Project Project is open ended. The following are examples of projects that we are currently working on but we may have changed by June next year. 1. Optimising a portfolio of Liquified Natural Gas contracts which contain optionality together with ship scheduling 2. Predicting hourly power (electricity) prices from the base-load and peak-load power prices as well as other factors such as the power-coal and power-gas spread
Skills Required Optimisation, Statistics
Skills Desired Mathematical Finance, Stochastic Calculus, Numerical Analysis, Python Programming


Too Many Traders Spoil the Return: How to Identify Crowded Strategies and Trades

Contact Name Charlotte Grant
Contact Email
Company Name Oxford Asset Management
Address OxAM House 6 George Street
Period of the Project 8 weeks
Project Open to Undergraduates, Part III students, PhD students
Deadline to Register Interest 22 February
Brief Description of the Project When too many traders have exposure to similar positions, it becomes crowded. Such positions may have a lower expected return or higher expected risk, particularly when a few large players dominate a stock's liquidity or wish to exit a position at a similar time. The project will involve using market level transaction data to identify the temporal dynamics of crowded positions through studying intra-stock correlations on various time scales. If time allows, we will augment this analysis with metrics derived from other financial datasets.
Skills Required Python programming. Experience and interest in statistics and probability.
Skills Desired