POSTDOCTORAL INNOVATION RESEARCH ASSOCIATE FELLOWSHIP IN MACHINE LEARNING CLASSIFIERS FOR MENTAL HEALTH DIAGNOSTICS
Saccade Diagnostics is a multiple award winning spin out of the University of Aberdeen focused on developing eye movement tests as a Point of Care diagnostic tool (SaccScan) to assist clinical assessment, referrals and management of different psychiatric disorders at the level of the individual patient.
Mental ill health is now recognised as the largest cause of short and long term disability worldwide costing the global economy US$2.5T in 2010 (€798 billion in Europe), with a projected increase to over US$6T by 2030, a CAGR of 7.0%. 1 in 5 of us experience mental health problems during our lifetime but more than half of patients don’t receive adequate care at first presentation of illness. Treatments are available that allow patients to resume normal functioning in society but clinicians are struggling to make accurate diagnosis, match therapy to condition, and provide timely care. Currently, when patients’ symptoms and behaviour don’t meet the criteria set out in the diagnostic manual, it may take up to 10 years to diagnose the illness. Delays in receiving a diagnosis can significantly impede delivery of the most effective treatment plan, exposing the patient to risk of further deterioration in well-being, reduction in quality of life leading to job loss, family breakdown, and self-harming.
SaccScan has been demonstrated to detect schizophrenia with better than 95% accuracy and has been extended with the same precision to bipolar disorder and major depression illnesses. The test can be performed within 30 to 45 minutes and results produced over the internet at near real-time speed. Built on existing eye tracking technology, and access to proprietary clinical reference databases, SaccScan successfully utilises eye movement abnormalities as clinical diagnostic biomarkers for serious mental illnesses. Early economic modelling showed that introducing SaccScan into health care services could produce savings of €40k per patient in the case of suspected schizophrenia alone.
We are backed by the Wellcome Trust (the largest biomedical research charity in the world), the Department of Health (responsible for administering the NHS - the largest healthcare system in the world) and several other agencies including the European Commission through H2020. We are partnered with world leading companies in their fields within the supply chain. We are collaborating with multiple field validation sites on an international scale who will provide the necessary test bed environment to validate our prototypes in addition to current longitudinal clinical studies across labs in Scotland between Aberdeen, Glasgow and Edinburgh.
This is an opportunity for an experienced professional researcher to join our world leading team in developing next generation machine learning (ML) classifier models to help interpret eye movement abnormalities in a range of psychiatric illnesses. The Innovation Research Associate will work with eye movement data previously obtained from patients as well as healthy controls. A working schizophrenia model will be used as a starting point for looking at other illnesses. Reporting directly to the CTO and working in close conjunction with our clinical and production teams, the Innovation Research Associate will be responsible for championing our research initiatives and supporting our cross-functional organisation. The Innovation Research Associate will drive the forward thinking approach on the use of the SaccScan technology that will influence the scalability of our product line and overall direction of the company.
Skills / Qualifications:
- Already completed a PhD in psychiatry, cognate science, mathematics, biostatistics, or computational statistics
- Experience in theory and practical applications of machine learning, statistical inference and data mining
Highly desirable knowledge and skills base:
- Software engineering to ISO standards and Medical Device Directives
- Bayesian methods
- Statistical dimensionality reduction
- Spatial statistics
- A working knowledge of psychological theories of mental health would be advantageous
- Time management skills and ability to work independently while ensuring deadlines are consistently met
- Ability to communicate effectively with non-technical colleagues and external parties (partners, users) including Plain English written communication skills
Essential programming experience using source code and library functions for:
- Supervised machine learning techniques (principally MATLAB) to build predictive classifiers including missing data imputation, classifier optimisation using ROC operating points and cross-validation
- Data visualisation (MATLAB, R, C/C++, Python, Java, OpenGL)
- Experience working in an interdisciplinary environment
REQUIRED LANGUAGES: ENGLISH: Excellent
- Generous salary package in line with researcher career level
- Statutory insurance coverage
- Relocation to Scotland
- Tailored training programne
- Journal publication authorship
- International work assignments
- Presentations at high impact conferences
Achieved minimum R2 level according to the European Framework for Researcher Career definitions:
- R1 First Stage Researcher - up to the point of PhD;
- R2 Recognised Researcher - PhD holders or equivalent who are not yet fully dependent;
- R3 Established Researcher - researchers who have developed a level of independence; and
- R4 Leading Researcher - researchers leading their research area or field. Have demonstrated expertise in line with the job requirements.
Comply with the Transnational Mobility Criteria: the researcher must come from a different country than the place of employment (irrespective of his/her nationality). This means that at the time of recruitment (September 2017), researchers must have resided or carried out their main activity (work, studies, etc.) in the country where the company is established for no more than 12 months in the 3 years immediately before the reference date (from 01/09/2014 until 31/08/2017). Compulsory national service and/or short stays such as holidays are not taken into account.
This is an exciting time to be joining our fast growing company in Edinburgh. Following successful completion of the fellowship there will be opportunities to join us permanently as we seek to expand our product development team. There are significant prospects for growth into leadership roles within the organisation.
Email CV, cover letter and copy of most relevant publication(s) to firstname.lastname@example.org.