PhD Studentship in AI Driven Population Health Study
PhD Studentship in AI Driven Population Health Study : Improving medication verification for cancer patients
Applications are invited for a three-year PhD studentship. The studentship will start on 1 October, 2021, or as soon as possible after that.
Medication errors, including those in prescribing, dispensing, or administration of a drug, are the single most preventable cause of patient harm. They have a significant impact on the efficiency of the workflow in pharmacy, raise safety concerns for patients, and result in a financial burden on the healthcare systems. Within cancer treatment, emphasis on reducing the number of medication errors has been an active research area for many years, with understanding that interdisciplinary approaches are vital to assure continuous improvement. Opportunities created by the reduction of transaction times for complex computational processes and use of machine learning to support clinical decision making, create a potential catalyst for the development of tools for reduction in medication errors.
This PhD studentship offers an exciting opportunity of exploring AI and machine learning with large clinical data sets residing within electronic health records to create methods to assure the effective use of systemic anticancer treatment (including traditional cytotoxic chemotherapy, immunotherapy, novel oral therapies etc.) without compromising patient safety. The studentship will require application of interdisciplinary skills to enable cooperation between the research, clinical, industry and patient communities in the development of a novel approach which could enhance clinical outcomes.
Professor Shang-Ming Zhou
- Dr Edward Meinert (email@example.com)
- Mrs Andrea Preston (Andrea.Preston@uhbw.nhs.uk)
This PhD student will be academically advised by Professor Shang-Ming Zhou and Dr Edward Meinert, research scientists with research interests in applied artificial intelligence and machine learning, computing science in health and care. The student will also be advised by Mrs Andrea Preston, a Macmillan Divisional Lead Haematology & SW Cancer Commissioning Pharmacist. This supervision team will assure the execution of a world-class PhD embedded into the wider digital health ecosystem at the University of Plymouth.
· This PhD studentship is offered for UK and international applicants.
· Applicants should have:
1) A first or upper second-class honours degree, and a relevant Master’s qualification in Computing Science, Data Science, Statistics, Health Informatics, Medical Informatics, Bioinformatics, or any areas related;
2) Interest in working with real-world problems and large data sets;
3) Excellent proficiency in English and outstanding communication skills;
4) Strong analytical and programming skills;
5) A “can do”, positive attitude with an aspiration to change the world.
· Experience in machine learning is advantageous.
· Experience in publication of peer-reviewed literature is desirable.
International applicants should meet the English language requirements, please see the details from the University’s website https://www.plymouth.ac.uk/international/how-to-apply/english-language-requir ements . IELTS Academic 6.5 or above (or equivalent) with 5.5 in each individual category is commonly required by the University’s Doctoral College.
How to Apply
To apply for this position, please visit: https://www.plymouth.ac.uk/student-life/your-studies/research-degrees/postgra duate-research-studentships/improving-medication-verification-for-cancer-pati ents-a-pragmatic-ai-driven-population-health-study .
Please clearly state the name of the studentship that you are applying for on your Personal Statement.
A research proposal is required.
Please see: https://www.plymouth.ac.uk/student-life/your-studies/research-degrees/applica nts-and-enquirers
for a list of supporting documents to upload with your application.
If you wish to discuss this project further informally, please contact Professor Shang-Ming Zhou (firstname.lastname@example.org), Dr Edward Meinert (email@example.com), or Mrs Andrea Preston ( Andrea.Preston@uhbw.nhs.uk).
For more information on the admissions process, please contact firstname.lastname@example.org.
The closing date for applications is 30 July 2021. Shortlisted candidates will be invited for interview.
Professor in e-Health,
Fellow of the Higher Education Academy,
Centre for Health Technology,
Faculty of Health,
University of Plymouth,
Plymouth, PL4 8AA, UK
Email: email@example.com ; firstname.lastname@example.org
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