Research Assistant (Quantitative)

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

We are looking for research assistants with a quantitative background for ongoing research in Public Health. They will be working within the team under the Principal Investigators Associate Professor Alex R Cook and/or Assistant Professor Borame Dickens.

Candidates need to be able to understand statistical modelling, have a mathematical background, and be fluent in R programming. We will also consider candidates who have extensive C++ or Python coding knowledge as these are transferrable to R.

The candidate will be working with the Principal Investigator(s) on the analysis of national health datasets, utilising an array of methods to infer statistical relationships and health outcomes. Further mathematical modelling will also be carried out when necessary involving diagnostic flows and where appropriate, disease spread and/or illness progression.

The Principal Investigator(s) is seeking for an independent worker who is well-organized, analytical and codes competently. They will however be receiving support from a team of mathematicians, epidemiologists and statisticians, and have a diverse portfolio of tasks. Under the team’s guidance, they will be expected to co-lead their own publications. We welcome academic creativity and will be highly supportive of candidates who wish to either pursue academia or desire for career progression provided they show self-motivation to showcase their problem-solving abilities.

Responsibilities:

• Disease modelling
• Statistical analyses
• Academic writing and publication of results
• Preparation of meeting materials for stakeholders

Requirements:

• Completed a BSc in a quantitative discipline (statistics, mathematics, computational biology, data science).
• Strong programming skills (R preferred)
• Statistical competence (Bayesian is advantageous)