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Biofilms are complex microbial communities that attach to surfaces and thrive in diverse environments. Microbial biofilms have a global impact across a wide range of industrial sectors, valued in excess of $5tn per annum.
While biofilms have beneficial applications in biotechnology, they also pose significant challenges, including biofilm infections in healthcare settings and biofilm fouling in industrial systems. These challenges result in profound economic and societal impacts, such as increased healthcare costs, reduced operational efficiency and environmental concerns.
Understanding and predicting biofilm growth, structure, and mechanics under changing environmental conditions is essential for addressing these issues.
This PhD project will focus on developing cutting-edge digital twin technologies to model biofilm behavior. Digital twins are virtual representations of physical systems that integrate real-world data and simulations to predict outcomes.
By leveraging physics-based simulations and machine learning, this project will simulate how environmental factors such as nutrient availability, shear stress and temperature influence biofilm growth and mechanics. These insights will provide actionable solutions for preventing biofilm infections and mitigating fouling in industrial processes.
Biofilms are complex microbial communities that attach to surfaces and thrive in diverse environments. Microbial biofilms have a global impact across a wide range of industrial sectors, valued in excess of $5tn per annum.
While biofilms have beneficial applications in biotechnology, they also pose significant challenges, including biofilm infections in healthcare settings and biofilm fouling in industrial systems. These challenges result in profound economic and societal impacts, such as increased healthcare costs, reduced operational efficiency and environmental concerns.
Understanding and predicting biofilm growth, structure, and mechanics under changing environmental conditions is essential for addressing these issues.
This PhD project will focus on developing cutting-edge digital twin technologies to model biofilm behavior. Digital twins are virtual representations of physical systems that integrate real-world data and simulations to predict outcomes.
By leveraging physics-based simulations and machine learning, this project will simulate how environmental factors such as nutrient availability, shear stress and temperature influence biofilm growth and mechanics. These insights will provide actionable solutions for preventing biofilm infections and mitigating fouling in industrial processes.
Admission Requirements
3.2+
6.5+
92+
We are seeking an ambitious and motivated candidate who meets the following criteria:
Holds, or expects to achieve, a 2:1 or 1st class degree (or international equivalent) in a relevant field such as bioengineering, mechanical engineering, computational biology, computer science, materials science, physics or a related discipline.
Demonstrates programming experience. Familiarity with Python and C++ is highly desirable but not essential.
12 Feb 2025
3 Years
Oct
International
28,600
Scholarships
Selecting the right scholarship can be a daunting process. With countless options available, students often find themselves overwhelmed and confused. The decision can be especially stressful for those facing financial constraints or pursuing specific academic or career goals.
To help students navigate this challenging process, we recommend the following articles:
Digital Twins for Biofilm Management PhD
Loughborough University, Loughborough, United Kingdom
# 151-200QS Subject Rankings
36 monthsProgramme duration
28,600 Tuition Fee/year
12 Feb, 2025Application Deadline
Programme overview
Main Subject
Materials Sciences
Degree
PhD
Study Level
PHD
Study Mode
On Campus
Digital Twins for Biofilm Management PhD
Biofilms are complex microbial communities that attach to surfaces and thrive in diverse environments. Microbial biofilms have a global impact across a wide range of industrial sectors, valued in excess of $5tn per annum.
While biofilms have beneficial applications in biotechnology, they also pose significant challenges, including biofilm infections in healthcare settings and biofilm fouling in industrial systems. These challenges result in profound economic and societal impacts, such as increased healthcare costs, reduced operational efficiency and environmental concerns.
Understanding and predicting biofilm growth, structure, and mechanics under changing environmental conditions is essential for addressing these issues.
This PhD project will focus on developing cutting-edge digital twin technologies to model biofilm behavior. Digital twins are virtual representations of physical systems that integrate real-world data and simulations to predict outcomes.
By leveraging physics-based simulations and machine learning, this project will simulate how environmental factors such as nutrient availability, shear stress and temperature influence biofilm growth and mechanics. These insights will provide actionable solutions for preventing biofilm infections and mitigating fouling in industrial processes.
Programme overview
Main Subject
Materials Sciences
Degree
PhD
Study Level
PHD
Study Mode
On Campus
Digital Twins for Biofilm Management PhD
Biofilms are complex microbial communities that attach to surfaces and thrive in diverse environments. Microbial biofilms have a global impact across a wide range of industrial sectors, valued in excess of $5tn per annum.
While biofilms have beneficial applications in biotechnology, they also pose significant challenges, including biofilm infections in healthcare settings and biofilm fouling in industrial systems. These challenges result in profound economic and societal impacts, such as increased healthcare costs, reduced operational efficiency and environmental concerns.
Understanding and predicting biofilm growth, structure, and mechanics under changing environmental conditions is essential for addressing these issues.
This PhD project will focus on developing cutting-edge digital twin technologies to model biofilm behavior. Digital twins are virtual representations of physical systems that integrate real-world data and simulations to predict outcomes.
By leveraging physics-based simulations and machine learning, this project will simulate how environmental factors such as nutrient availability, shear stress and temperature influence biofilm growth and mechanics. These insights will provide actionable solutions for preventing biofilm infections and mitigating fouling in industrial processes.
Admission Requirements
International
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To help students navigate this challenging process, we recommend the following articles:
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Looking for a fully-funded scholarship to see you into university? Find out how to boost your chances of getting one.
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Using the Metabolome to Study Aging and Geroprotectors in 3D Cell Models PhD
Visual Data Mining on Proteins and Biomedicine Crystallisation PhD
Waste-derived Bioplastics for a Circular and Sustainable Food Packaging Economy PhD
Life Sciences and Medicine (6)
Adaptive and Predictive Computational Modelling of Biomaterials: A Digital Twin Framework for Next-Generation Personalised Healthcare PhD
Adaptive and Predictive Computational Modelling of Biomaterials: A Digital Twin Framework for Next-Generation Personalised Healthcare PhD
Biomarkers of Aging PhD
Biomarkers of Aging PhD
Developing Prescription Guidelines for Passive Heat Therapy PhD
Developing Prescription Guidelines for Passive Heat Therapy PhD
Does Socialised Real-Life Body Exposure Promote Body Image Positivity PhD
Does Socialised Real-Life Body Exposure Promote Body Image Positivity PhD
Energy Dissipation In Human Soft Tissue During Impacts PhD
Energy Dissipation In Human Soft Tissue During Impacts PhD
Natural Sciences (6)
Accelerated Screening and Synthesis of New Chemotherapeutic Agents for Pancreatic Cancer PhD
Accelerated Screening and Synthesis of New Chemotherapeutic Agents for Pancreatic Cancer PhD
Bespoke Organometallic Compounds for Anticancer Studies PhD
Bespoke Organometallic Compounds for Anticancer Studies PhD
Bio-based Sustainable Materials for Autonomous Thermal Management in Wearables PhD
Bio-based Sustainable Materials for Autonomous Thermal Management in Wearables PhD
Biomimmetic Catalytic Asymmetric Synthesis of the Bisorbicillinoids and Related Products PhD
Biomimmetic Catalytic Asymmetric Synthesis of the Bisorbicillinoids and Related Products PhD
Climate and Ecological Transitions Hub Studentship PhD
Climate and Ecological Transitions Hub Studentship PhD
Coherent State Methods in Spectral Asymptotics PhD
Coherent State Methods in Spectral Asymptotics PhD
Computational Modelling of Sustainable Alloys PhD
Designing Classroom Tasks To Improve Students’ Learning Of Mathematics PhD
Developing Novel Antibiofilm Coatings for Sustainable Biofilm-Free Surfaces PhD
Development of Hybrid Microcomb Sources with Topologically Protected Dark Resonances for Simultaneous Classical and Quantum PhD
Development of Novel Bicyclopentanes (BCP) Bioisosteres From Bench Stable [1.1.1]-Propellane Precursors PhD
Digital Twins for Biofilm Management PhD
Dispersionless Integrable Systems In 2+1 Dimensions PhD
Social Sciences and Management (6)
An Evaluation of Erasmus+ Sport Projects and Their Impact on EU Sport Policy Development PhD
An Evaluation of Erasmus+ Sport Projects and Their Impact on EU Sport Policy Development PhD
Climate Change, Work Design and Worker Wellbeing Phd
Climate Change, Work Design and Worker Wellbeing Phd
Communication and Social Change (CSC) Hub Studentship PhD
Communication and Social Change (CSC) Hub Studentship PhD
Enhancing Motivation in Sport and Exercise Contexts PhD
Enhancing Motivation in Sport and Exercise Contexts PhD
Out of School Youth Provision: An Analysis of Funding and Operational Resilience within the Sector PhD
Out of School Youth Provision: An Analysis of Funding and Operational Resilience within the Sector PhD