Research Fellow in the Department of Basic & Clinical Neuroscience

at  Kings College London

Job id: 097505. Salary: £52,874 - £61,921 per annum, including London Weighting Allowance.
Posted: 25 October 2024. Closing date: 17 November 2024.
Business unit: IoPPN. Department: Basic & Clinical Neuroscience.
Contact details: Ammar Al-Chalabi. ammar.al-chalabi@kcl.ac.uk
Location: Denmark Hill Campus. Category: Research.
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About us
We are part of the King’s MND Care and Research Centre, a prize-winning internationally recognized group of clinical and laboratory research teams working to accelerate the search for a cure for motor neuron disease (also known as ALS). We work across King’s College London and King’s College Hospital at the Denmark Hill site.
About the role
This post sits within the Department of Basic and Clinical Neuroscience ( www.kcl.ac.uk/neuroscience/about/departments/basic-clinical-neuroscience), working closely with Biostatistics and Health Informatics ( https://www.kcl.ac.uk/bhi), and will be part of a team working to digitally simulate populations of people with motor neuron disease for the purposes of clinical trial design and optimisation.
The role sits within the UK Motor Neuron Disease Research Institute ( https://ukmndri.org) and the NIHR BRC Motor Neuron Disease theme ( www.maudsleybrc.nihr.ac.uk/research/motor-neuron-disease-mnd/), and is part of the Precision ALS programme in the TRICALS Consortium, Europe’s largest MND research initiative.
We are looking for an independent thinker who wants to be part of a growing team to lead statistical methodology and improve the efficiency of trial designs within the MND space. You will improve the generalisability of observational and interventional studies through the generation of digital twins of patients.
You will work with the statistical, computing and clinical teams to develop and test a statistical digital twins model in the neurodegenerative condition motor neuron disease (MND, ALS), including performing internal and external validation of the model and running simulations, handling clinical trial data compliant with data, confidentiality and ethics rules, working within a Trusted Research Environment to access clinical data.
You should be capable of performing the clinical trial design and virtual analyses planned, based on the modelling. Methods will include development of digital twins using Cox regression, simulation, deep learning, AI, and large language models, with validation against real world populations from a national register, clinical trials and clinic registers, including the ability to model the natural biases of each data source.
You will work closely within the multidisciplinary clinical and research teams, including the King’s Trials Methodology team ( www.kcl.ac.uk/research/trials-methodology-research-group)), reporting to Professor Ammar Al-Chalabi.
This is a full-time post (35 Hours per week), and you will be offered a fixed term contract until 31/03/2026 in the first instance.
About you
To be successful in this role, we are looking for candidates to have the following skills and experience:

Essential criteria

• PhD in Statistics, Biometry, Medical or Applied Statistics.
• Considerable understanding of epidemiology and medical statistics including, longitudinal analysis and handling of missing data
• Knowledge/experience in the conduct of all stages of a randomised controlled trials (RCT) from conception to dissemination
• To have independently worked up a grant submission from conception to submission
• Experience of working as the technical expert in a multidisciplinary team

Desirable criteria

• Experience in handling clinical datasets
• Experience with “big data”
• Experience in statistical modelling
• Experience as the statistical lead in published medical research
• To have published in leading clinical journals with a clear position of contribution commensurate with a Research Excellence Framework of at least nationally relevant impact

Downloading a copy of our Job Description
Full details of the role and the skills, knowledge and experience required can be found in the Job Description document, provided at the bottom of the next page after you click “

Please refer the Job description for details