Job title Mathematical Innovation Research Assistant (fixed-term)

Department Institute for Mathematical Innovation

Salary Starting from £29,605, rising to £36,024

Grade Grade 6

SOC Code - Visa Requirements 2119

This role is potentially sponsorable subject to candidates meeting the tradeable points criteria

Placed on Thursday 16 November 2023

Closing date Thursday 23 November 2023

Interview date To be confirmed

Reference ED11146

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Project overview:

We are developing an advanced mathematical tool tailored for enhancing soil moisture forecasting across various depths at the monitoring stations of Cemaden, Brazil's Natural Disaster Prediction Centre.

This unique opportunity grants hands-on experience in manipulating real-world data, applying intricate mathematical modelling, and delivering impactful, real-world results, pivotal for disaster prediction in São Paulo.

Background:

Cemaden's monitoring stations (known as PCDs Geo) come equipped with rain gauges and six moisture sensors, registering soil moisture at intervals of 50cm, spanning from 50cm to 3m in depth.

Harnessing historical data from these apparatuses, our methodology involves data inversion via a straightforward infiltration programme.

This process can be used to infer geotechnical parameters. However, given that direct calculations hinge on a homogeneous medium assumption, tailored inversions for every individual sensor at each station are important.

Our goal is to effectively combine these parameters with rainfall forecasts to yield precise soil moisture predictions.

Our preliminary outcomes, recently showcased in Sousa et al. (2023), show the potential of this research.

Key responsibilities of the role:

Develop the automated system development for retrieving rainfall and soil moisture data from Cemaden's platform.

Explore and test different infiltration models, leaning towards models considering distinct soil layers as opposed to purely homogeneous ones.

Create an inversion programme able to handle data from a multitude of stations, either autonomously or semi supervised.

Formulate mathematical algorithms in Python able to:

Ascertain real-time rainfall data.

Acquire rainfall forecasts.

Predict imminent soil moisture for each PCD (with 105 stations presently operational).

Transmit the data back into Cemaden's system, destined for integration into São Paulo's public alert disaster prediction software.

Desired qualifications:

Enrolled as a PhD student in Mathematics or equivalent, preferably in advanced stages. A solid foundation in mathematical modelling and data inversion. Fluent in Python programming. Prior exposure or a keen interest in geotechnical or environmental data would be advantageous.

Additional perks:

Potential opportunity to visit Cemaden's facilities and interact directly with experts in the field. Direct contributions to be integrated into the core disaster prediction software, enhancing safety measures for São Paulo's populace.

This position provides the opportunity to gain hands-on experience working in industry and will enhance your CV.

What we can offer you:

a very generous employer contributory pension scheme

generous annual leave allowance with an additional 5 discretionary days so that you can enjoy a positive work life balance

we are a family-friendly University and with an increasingly agile workforce, are open to flexible working arrangements

an excellent reward package that recognises the talents of our diverse workforce

a wide range of personal and professional development opportunities

a number of support options available for new and existing staff to help with the cost of some immigration expenses which you may be eligible for: Relocation allowance, Visa Reimbursement, Interest-Free Loan.

We aim to be an inclusive university, where difference is celebrated, respected and encouraged. We have an excellent international reputation with staff from over 60 different nations and have made a positive commitment towards gender equality and intersectionality receiving a Silver Athena SWAN award. We truly believe that diversity of experience, perspectives, and backgrounds will lead to a better environment for our employees and students, so we encourage applications from all genders, backgrounds, and communities, particularly from under-represented groups, and value the positive impact that will have on our teams.

We are very proud to be an autism friendly university and are an accredited Disability Confident Leader; committed to building disability confidence and supporting disabled staff.

Find out from our staff what makes the University of Bath a great place to work. Follow us @UniofBath and @UniofBathJobs on Twitter for more information.

Further details:

Job Description & Person Specification

Terms and Conditions of Employment

We are constantly seeking to reduce the unconscious bias that enters any assessment process, with the goal of creating an inclusive and equal assessment process. To support this, personal details will be removed from application forms at the initial shortlisting stage.