or
Looking to list your PhD opportunities? Log in here.
This project is devoted to an AI-based prediction of bacteria growth and its control by antibiotics. In synthetic biology, an improved understanding of bacterial regulatory circuits is required to develop complex biological systems with functionalities beyond existing in nature [1, 2]. Mathematical prediction of the behaviour of such systems is highly problematic because of the lack of reliable mathematical models and the sparsity of dynamic data. Such systems depend on numerous parameters with uncertainties. On the other hand, mathematical modelling of bacterial cooperative behaviour potentially is very attractive since it is much less costly and time-consuming than laboratory experiments.
To obtain a data-driven model, the dynamic mode decomposition, originally proposed by Schmid [3], is proposed to be used. DMD is a purely data-driven approach that only requires snapshots over time of measurement data at differently spaced control points. It does not require any preliminary mathematical model because it is supposed that DMD is originally trained on large enough data. The attractive feature of DMD is that it allows the analysis of dynamic data via their expansion into basic dynamic modes, with each mode characterised by its own frequency. Thus, DMD can capture key coherent spatio-temporal patterns from complex data. In addition, the method of optimal prediction [4] will be implemented to reduce the effect of uncertainties caused by incomplete data.
This approach effectively provides the expectation under uncertainties. To obtain the expectation for the bacterial growth, random initial populations should be considered. The use of DMD with the optimal prediction should allow the prediction to be achieved very efficiently, avoiding the time-consuming Monte Carlo method of classical ensemble modelling [5]. The developed algorithm should be compared with alternative approaches such as the supervised machine learning and existing mathematical models.
Eligibility
Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.
Funding
At Manchester we offer a range of scholarships, studentships and awards at university, faculty and department level, to support both UK and overseas postgraduate researchers applying for competition and self-funded projects.
For more information, visit our funding page or search our funding database for specific scholarships, studentships and awards you may be eligible for.
Before you apply
We strongly recommend that you contact the supervisors for this project before you apply.
How to apply
Apply online through our website: https://uom.link/pgr-apply-fap
When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.
Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.
After you have applied you will be asked to upload the following supporting documents:
Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact.
We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.
We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).
The university will respond to you directly. You will have a FindAPhD account to view your sent enquiries and receive email alerts with new PhD opportunities and guidance to help you choose the right programme.
Log in to save time sending your enquiry and view previously sent enquiries
The information you submit to The University of Manchester will only be used by them or their data partners to deal with your enquiry, according to their privacy notice. For more information on how we use and store your data, please read our privacy statement.
Research output data provided by the Research Excellence Framework (REF)
Click here to see the results for all UK universitiesBased on your current searches we recommend the following search filters.
Check out our other PhDs in Manchester, United Kingdom
Start a New search with our database of over 4,000 PhDs
Based on your current search criteria we thought you might be interested in these.
Optimal prediction of dynamical systems with incomplete data
The University of Manchester
Early-Stage Carbon Prediction for Olympic Park Development: A Data-Driven Approach for Low-Carbon Infrastructure and Sustainable Legacy
Kingston University
Data-driven population dynamics for biofiltration
University of Birmingham