Job Description

Job id: 070166. Salary: Research 6, G6- \xc2\xa341,386 - \xc2\xa348,414/ Research Fellow G7 - \xc2\xa349,737 - \xc2\xa358,421 per annum, including London Weighting Allowance.

Posted: 29 June 2023. Closing date: 23 August 2023.

Business unit: IoPPN. Department: Psychosis Studies.

Contact details: Prof Nikolaos Koutsouleris/Dr Paris Alexandros Lalousis. nikolaos.koutsouleris@kcl.ac.uk/paris.lalousis@kcl.ac.uk

Location: Denmark Hill Campus. Category: Research.

Job description

We are looking for a researcher with a background in psychology, psychiatry, biomedical engineering or a related discipline to join the Department of Psychosis Studies at the Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King\'s College London, as Postdoctoral Research Associate.

The successful applicant will be expected to develop and use machine learning strategies in order to train and validate predictive models of psychosis and affective disorders using neuroimaging (e.g., structural, functional MRI, spectroscopy, PET), omics data (e.g., genomic, proteomic, cytokine markers) and digital phenotyping (e.g., ecological momentary assessments, passive sensing) datasets.

Furthermore, the successful applicant is expected to develop a predictive modelling platform at the Department of Psychosis studies to host and deploy the trained machine learning models for the purpose of transdiagnostic comparisons, external validation, and integration in future stratified clinical trials.

This post will be offered on an a fixed-term contract for 3 years (Research Associated)/2.5 years (Research Fellow)

This is a full-time post - 100% full time equivalent

Key responsibilities

Research (70%)

• To develop and use machine learning strategies to train and validate diagnostic and prognostic models of psychosis and affective disorders using different neuroimaging (e.g., structural, functional MRI, spectroscopy, PET) , data (e.g., genomic, proteomic, cytokine markers) and digital phenotyping (e.g. ecological momentary assessments, passive sensing) datasets.
• Write clean and efficient code to preprocess, analyse, and interpret complex mental health data, ensuring data privacy and security.
• To explore the neuroimaging, omics data and digital phenotyping correlates of psychopathological and neurocognitive disease dimensions across psychotic and affective disorders.
• To help develop and sustain an IT infrastructure at the Department of Psychosis Studies that allows to test the utility of diagnostic, prognostic, and predictive models in applied clinical research (e.g. stratified clinical trial designs).
• Collaborate closely with a multidisciplinary team of clinicians, psychologists, and researchers to understand their needs and develop innovative machine learning software solutions.
• To prepare scientific reports for publication in peer-reviewed journals
• To support effective collaboration between different research teams at the IoPPN, and between the Chair\'s IoPPN team and team members at the Department of Psychiatry and Psychotherapy at Ludwig-Maximilian-University Munich & Max-Planck Institute of Psychiatry in Munich.
• Active participation in project-related meetings and contributions to planning and engagement on key projects.
• The successful candidate will also be encouraged to develop grant and fellowship applications to establish themselves in this area of research in the long-term

Teaching (25%)

• Occasional teaching and supervision of postgraduate students will be encouraged
• To provide training, guidance, and support to junior research staff as appropriate.

Administration (5%)

• To contribute to administrative duties as directed

The above list of responsibilities may not be exhaustive, and the post holder will be required to undertake such tasks and responsibilities as may reasonably be expected within the scope and grading of the post.

Skills, knowledge, and experience

Essential criteria for Research Associate G6 * PhD in neuroscience, psychology, psychiatry, biomedical engineering, related discipline.

• Documented expertise and an established track record in machine learning across different data domains of mental / neurological disorders.
• Experience in analyzing high-dimensional data such as structural, functional neuroimaging, omics information, or temporal data as recorded using smartphones, wearable devices.
• Excellent statistical and coding skills with demonstrated ability to apply and combine methodologies across MATLAB, Python, R.
• Highly motivated and enthusiastic researcher with a strong and documented interdisciplinary interest in mental health,
• Strong evidence of potential to build an academic career trajectory, including track record of publishing in scientific journals and participation in research projects, grants, and fellowships,
• Interpersonal and communication skills with demonstrated ability to work within a geographically distributed team (London and Munich),
• Excellent organizational skills and ability to work independently and within teams to meet agreed deadlines and achieve project goals.
• Proven experience in developing and implementing machine learning models and algorithms, ideally in the healthcare, mental health domain.

Desirable criteria for Research Associate G6 * Track record of successful research grant applications, attempts to obtain grant funds.

• Previous development of ML-based software such as recommendation systems, computer-aided decision support systems
• Previous experience with using deep learning models (e.g. convolutional neural networks, autoencoders, transformers) for academic research
• Documented experience in using coding and data management systems such as e.g. GitHub, DataLad, Amazon Web Services (AWS), Microsoft Azure.
• Demonstrated ability to co-supervise and mentor students at PhD and MSc levels.

Essential criteria for Research Fellow G7 * PhD in neuroscience, psychology, psychiatry, biomedical engineering, related discipline.

• Documented expertise and an established track record in machine learning across different data domains of mental or neurological disorders.
• Experience in analyzing high-dimensional data such as structural / functional neuroimaging, omics information, temporal data as recorded using smartphones, wearable devices.
• Documented experience in using coding and data management systems such as e.g. GitHub, DataLad, Amazon Web Services (AWS), Microsoft Azure.
• Excellent statistical and coding skills with demonstrated ability to apply and combine methodologies across MATLAB, Python, or R.
• Highly motivated and enthusiastic researcher with a strong and documented interdisciplinary interest in mental health.
• Strong evidence of potential to build an academic career trajectory, including track record of publishing in scientific journals and participation in research projects, grants, and fellowships as well as a demonstrated ability to co-supervise and mentor students at PhD and MSc levels.
• Interpersonal and communication skills with demonstrated ability to work within a geographically distributed team (London and Munich).
• Excellent organizational skills and ability to work independently and within teams to meet agreed deadlines and achieve project goals.
• Proven experience in developing and implementing machine learning models and algorithms, ideally in the healthcare / mental health domain.
• Track record of successful research grant applications, and attempts to obtain grant funds.
• Previous development of ML-based software such as recommendation systems, computer-aided decision support systems as well as experience with using deep learning models (e.g. convolutional neural networks, autoencoders, transformers) for academic research.

Desirable criteria for Research Fellow G7 * Advanced machine learning expertise: Mastery of advanced machine learning techniques like deep learning, NLP, reinforcement, and causal machine learning.

• Proven history of delivering impactful projects and driving innovation, showcasing am ability to introduce cutting-edge methodologies, push boundaries, and consistently achieve high-quality results either in industry or academia.
• Proven experience in scalable data infrastructure and cloud technologies. Solid background in designing and implementing scalable data infrastructure solutions. This includes expertise in cloud platforms such as AWS, Azure, GCP, as well as proficiency in distributed computing frameworks like Apache Spark, Hadoop. Hands-on experience in leveraging cloud-based technologies to ensure efficient and reliable data processing and storage.

Please note that this is a PhD level role but candidates who have submitted their thesis and are awaiting award of their PhDs will be considered. In these circumstances the appointment will be made at Grade 5, spine point 30 with the title of Research Assistant. Upon confirmation of the award of the PhD, the job title will become Research Associate and the salary will increase to Grade 6.

Further information

Interview: TBC - 20th July 2023 (In-person, IoPPN)

We ask all candidates to submit a copy of their CV, and a supporting statement, detailing how they meet the essential criteria listed in the advert. If we receive a strong field of candidates, we may use the desirable criteria to choose our final shortlist, so please include your evidence against these where possible.

King\'s College London