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Current vacancies

 

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PhD Opportunity: A biochemo-mechano multi-scale computational model to predict bone adaptation over space and time

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About the Project

Musculoskeletal diseases as osteoporosis have huge impact on the mortality and morbidity of our ageing society. At the moment there are some pharmacological interventions for treating osteoporosis but they are not effective in all patients and their cost is very high. New interventions have to be tested in animal models before clinical studies, the mouse being one of the most used models. Nevertheless, animal alternatives such as advanced in vitro (e.g. cell cultures, organ on a chip methods) and in silico approaches (i.e. computational modelling and digital twins) can improve the design and testing of new interventions and partially replace animal experimentation. Bone adapts over time and space thanks to the activity of the bone cells, which are triggered by biomechanical (e.g. mechanical loading or disuse) and biochemical (e.g. diseases or pharmacological treatments) stimulation. Therefore, the bone adaptation process is very complex as it involves different dimensional scales: the mechanical loading the bone is subjected to due to external forces (body level), the deformation the bone is subjected to under those forces (organ-tissue level) and the mechanical and biochemical stimuli that the cells feel (cell levels).

The Finite Element approach based on biomedical images can be used to estimate accurately how bone deforms under external loads [1]. Biological networks based on Ordinary Differential Equations have been used to estimate how biochemical stimuli affects bone adaptation [2]. Nevertheless, these two approaches have not yet been combined into a multi-scale model to predict bone adaptation over time. This is due to the fact that it is challenging to combine these two approaches and it is even harder to validate the outputs of the models (i.e. compare with experimental data that measure the bone adaptation over time). In our groups we have collected longitudinal high-resolution images of bone adaptation over time in a mouse model treated with biomechanical and/or pharmacological interventions [3], providing the best validation datasets for the validation of the multi-scale models. Moreover, we have shown that mechano-regulation models (i.e. models that consider explicitly only biomechanical stimuli) can predict reasonably well only bone adaptation [4]. The hypothesis of this project is that multi-scale computational models that account for both biomechanical and biochemical stimuli can accurately predict bone changes over space and time due to different treatments. The project aims at developing the first multi-scale biomechanical model for the prediction of bone changes over time in the mouse tibia due to external biomechanical and biochemical stimuli, and at validating its outcomes versus state-of-the-art longitudinal micro-computed tomography (microCT) measurements of bone adaptation. The student will first perform a literature review and will be trained to use the finite element modelling approaches available at the supervisors’ teams. Then they will develop cell-level biological networks for the prediction of the changes in molecular and cellular concentrations over time due to biochemical stimuli and will integrate them with the finite element models. They will perform model verification and sensitivity analysis to identify the most important sensitive input parameters in the models. They will validate the models versus experimental measurements performed with in vivo longitudinal microCT imaging of the mouse tibia, in mice treated with pharmacological and/or biomechanical interventions. The student will evaluate the importance of accounting for mechanical and/or biochemical stimuli for the accurate prediction of bone changesdue to interventions. Finally, the student will focus on scientific publications and writing the thesis. The project will generate academic impact (using the model to test new hypothesis and the effect of combined treatments), industrial impact (for companies that develop treatments for the musculoskeletal system), and 3Rs impact (reduction and partial replacement of the usage ofanimals in research).

How to apply:

Please complete a University Postgraduate Research Application form available here: http://www.shef.ac.uk/postgraduate/research/apply.

Please clearly state the prospective main supervisor (Prof Enrico Dall’Ara) in the respective box and select SMPH Oncology and Metabolism as the department.

Funding Notes

Candidates must have a first or upper second class honors degree or significant research experience. Degree in Engineering, Physics, Mathematics or equivalent.

This studentship funded by the University of Sheffield will be 42 months in duration, and include home fee for students, stipend at UKRI rates, a research training support grant (RTSG) of £4,400. International student are welcome to apply only if they will have already co-funding for the difference between international and home fees. Overseas fees information is available on the website:

Fees for Academic Year 2024-25: View Website

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PhD Opportunity: Efficient in silico trials for bone diseases

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About the Project

The School of Mechanical, Aerospace and Civil Engineering at the University of Sheffield is recruiting for a PhD position associated with the EPSRC funded New Investigator Award BONESFE. The position will be based within the Integrated Musculo-Skeletal Biomechanics Group in the Insigneo Institute for in silico Medicine, a collaboration between the University of Sheffield, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield Children's NHS Foundation Trust and Doncaster and Bassetlaw Teaching Hospitals NHS Foundation Trust.

This project is concerned with the exciting new area of In Silico Trials. In Silico Trials aim to predict the safety and efficacy of medical interventions (i.e. devices and drugs), which are necessary to obtain regulatory approval such that these interventions can be brought to market. At the same time, In Silico Trials need fewer patients and/or shorter trial durations than conventional clinical trials and can reduce the cost to enter the market for much-needed medical products.

With a focus on bone diseases and drugs and devices used to treat them, the project will leverage recent advances in the characterisation of bone shape variation in a population and in the development of intrusive stochastic finite-element analysis tools. The overall aim of the project is to improve the efficiency and reduce the cost of conducing In Silico Trials, thus encouraging uptake of this technology by device and drug manufacturers.

We are looking for applicants who are eager to develop new skills and apply their existing knowledge. The project requires a good level of mathematical and solid mechanics background and will require the candidate to develop and extend existing finite-element code and execute it on high-performance computing systems. You should be excited to learn and develop new technology, be passionate about the subject and look to be creative in your work.

Applicants should hold or be completing this year a degree at a good level (2.1/1st or equivalent) in a related subject, e.g. engineering, physics, maths, computer science, and should be able to demonstrate good interpersonal and organisational skills. The expected start date for this project is September 2024.

Interested candidates are strongly encouraged to contact the project supervisor, Dr Pinaki Bhattacharya (p.bhattacharya@sheffield.ac.uk) to discuss your interest in and suitability for the project prior to submitting your application. 

To apply, please use our on-line PhD application form

Funding Notes

The funding for this opportunity includes fees set for UK (Home) applicants and a tax-free stipend of £19,237 per year, up to 3.5 years. Overseas applicants will need to cover the difference in fees from their own funds which is approximately £22,884 per year. Overseas applicants should ensure they can pay the difference in fees prior to applying or sending any enquiries.

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PhD opportunity: Transformable origami robots at Sheffield Microrobotics Lab

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We are excited to announce a funded PhD position at the Sheffield Microrobotics Lab, University of Sheffield, focusing on Inside-the-body Transformable Origami Robots. This opportunity is open exclusively to a UK/home student, starting in September/October 2024.

Research focus:

The project will explore the development of origami robots that can operate inside the human body. These robots will be designed to self-fold into functional structures capable of medical interventions. Applicants can expect to work on topics ranging from mechanical design and fabrication to the integration of smart materials that can sense and react to environmental stimuli.

Keywords: Self-folding origami robots, Inside-the-body programmable biomedical structures, Computational and cognitive smart structures and materials

Collaboration:

This PhD project involves collaboration with Prof. Frederik Claeyssens from the Department of Materials Science and Engineering, Prof. Daniela Rus’s group at the Computer Science and Artificial Intelligence Lab at MIT, and Dr. Dana Damian at the Sheffield Biomedical Robotics Lab.

Ideal candidate background: 

• Degrees in Mechanical Engineering, BioEngineering, Robotics, Material Sciences, or Computer Science.
• Interest in hands-on development of hardware and smart materials, but not limited to (e.g. computational structures).
• Ability to collaborate effectively within an interdisciplinary team.

Research environment: 

The Sheffield Microrobotics Lab boasts state-of-the-art facilities, including a dedicated cleanroom and extensive equipment for robotics research. The lab environment promotes active student interaction and is closely associated with Sheffield Robotics, a leading research center in the UK.

Funding details:

The position is supported by a full EPSRC DTP studentship available for a UK/home student.

Deadline: 

Please submit your applications by 30 June 2024 through the application portal. The selection process will continue until a suitable candidate is identified. Kindly notify Shuhei Miyashita in advance if you plan to apply, to ensure you receive the necessary support with your application.

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PhD Opportunity: Improving oxygen monitoring technology for young children and infants

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About the Project

Measuring oxygen levels in children is crucial, often considered as important as monitoring other vital signs like heart rate and temperature. However, doing so in infants and pre-schoolers outside of hospitals can be challenging.

Guidelines at both national and international levels suggest checking oxygen levels in primary care for children with respiratory issues to help decide if they need to be referred to a specialist. However, there's concern that the technology used for these measurements might be leading to unnecessary diagnoses, resulting in more hospital admissions for conditions like bronchiolitis in infants.

Surprisingly, the ability to monitor oxygen levels in children is not widely available in regular doctor's offices, even though respiratory infections are a common reason for children to visit primary care.

Finding simpler and more reliable methods to measure oxygen levels in children could not only improve decision-making in regular healthcare settings but could also be a valuable tool for monitoring at home, especially during potential respiratory pandemics. This issue is not just relevant to well-resourced areas but is also crucial in places where resources are limited, as pneumonia remains a leading cause of death in young children.

Aim

We will develop a smart material, e.g., fabric, sticker, as a custom interactive physical interface to improve sensing readings for oxygen measurements acquired at the tip of infants' and children's fingers, by finding suitable and seamless ways to engage the children in this process.

Key objectives

We will begin by conducting initial trials with children to examine different items like objects, fabrics, and toys. The goal is to figure out which interfaces could be effectively incorporated into a custom oxygen measurement device.

We'll work on developing a physical interface using electro-luminescence, inspired by the principles of oximeters, to enhance the accuracy of oxygen measurements. This interface also needs to fit well with how children naturally interact. The interface will be tested in real-world conditions, considering factors like flexion, bending, and stretching, while also assessing its performance on the smaller fingers of children.

Next, we'll implement an active interface to improve the sensing capabilities, along with setting up the necessary tools for data acquisition and ensuring portability.

To make the device engaging for a wide range of ages, we'll create various interfaces and evaluate their effectiveness with different age groups.

This project will give you the opportunity to work alongside patients, families, clinicians, engineers, and other experts in a pioneering cross-disciplinary programme to develop new digital platforms and technologies that can address unmet needs in child health.

This is a unique opportunity to be at the forefront of innovative developments in the field of paediatric digital healthcare technology and to make change happen for the better.

As a member of our team, you will receive training in fabricating flexible interfaces and functional materials, as well as the principles of wearables. These skills form the core of our laboratory's expertise. Our work on medical robots has garnered significant attention from both mainstream and specialised media outlets, including Forbes, The Economist, New Scientist, The Telegraph, and USA Today. National Geographic recognised our medical robots as one of the 12 Innovations that will revolutionise medicine in 2019, and Scientific American highlighted our work among the 10 Ideas that will change the world in 2016.

This project is a collaboration between Great Ormond Street Hospital for Children NHS Foundation Trust, Sheffield Children’s NHS Foundation Trust, the NIHR Children and Young People MedTech Co-operative, and the Insigneo Institute at the University of Sheffield.

Now it’s in its fourth 5-year term, a BRC National Paediatric Excellence Initiative has been set up between GOSH BRC and the children’s hospitals in Birmingham, Sheffield, and Liverpool. The GOSH BRC’s aim is to transform the health of children, and the adults they will become, by combining cutting-edge research methods with world-leading clinical trial expertise, to accelerate the discovery of new treatments for children with rare and complex conditions.

More information can be found here: https://sites.google.com/site/danadamian

Entry requirements

Candidates are expected to have a background in one of the following fields: Mechanical Engineering, Robotics, Mechatronics, Electrical Engineering, Material Engineering, Bioengineering, Control, or a related field. Applicants should have, or expect to achieve a first or upper second class UK honours degree or equivalent qualifications gained outside the UK in an appropriate area of study. 

How to apply

Please complete a University Postgraduate Research Application form available here: www.shef.ac.uk/postgraduate/research/apply

Please clearly state the prospective main supervisor Dr Dana Damian in the respective box and select Automatic Control & Systems Engineering as the department.

Enquiries

Pre-application and informal enquiries accompanied by a CV are encouraged to contact Sarah Black (Insigneo Administrative Manager, sarah.black@sheffield.ac.uk).

If you have questions about the project, feel free to email Dr Dana Damian (Primary Supervisor, d.damian@sheffield.ac.uk).

Deadline

Wednesday 10 July 2024

Interviews

Monday 22nd July 2024

Funding Notes

Funding is provided for home tuition fees only and a stipend (£18,622) for three years. Overseas tuition fees are not covered.
Funding is provided by the Insigneo Institute, Sheffield Children’s NHS Foundation Trust and NIHR GOSH Biomedical Research Centre (BRC) as part of their National Paediatric Excellence Initiative. The NIHR GOSH BRC (GOSH BRC) is a partnership between GOSH and the University College London (UCL) GOSH Institute of Child Health (ICH).