Research Fellow (Machine Learning for Cancer)

Job Purpose

The post holder will join a new Institute group in Machine Learning for Cancer.

The post holder will work with selected collaborators, drawn from the large base of internationally recognised research experts to design and implement computational statistical methodologies; advancing our understanding of cancer biology. The role will require the application of statistical and computational modelling skills to translate complex biological problems into a quantitative form. The post holder will use computational skills to implement statistical algorithms using modern computer programming languages and to use these to analyse data sets and interpret the results. Strong presentation and communication skills are required to convey complex information to both technical and non-technical experts.

The post holder will be embedded within the Statistical Machine Learning group, led by Dr Christopher Yau at the Centre for Computational Biology at the University of Birmingham but will require regular travel to the London office of the funding Institute.

Main Duty - Research

To plan and carry out research, using appropriate methodology and techniques, under the supervision of the Principal Investigator. This may include:

• Developing research objectives and proposals for own or joint research,
• Contribute to writing bids for research funding
• Analyse and interpret data,
• Apply knowledge in a way which develops new intellectual understanding
• Disseminate research findings for publication, research seminars etc
• Supervise students on research related work and provide guidance to PhD students where appropriate to the discipline,
• Contribute to developing new models, techniques and methods
• Undertake management/administration arising from research
• Contribute to Departmental/School research-related activities and research-related administration
• Contribute to enterprise, business development and/or public engagement activities of manifest benefit to the College and the University, often under supervision of a project leader
• Collect research data; this may be through a variety of research methods, such as scientific experimentation, literature reviews, and research interviews
• Present research outputs, including drafting academic publications or parts thereof, for example at seminars and as posters

Person Specification

• The candidate should have a first degree and a doctoral (PhD, DPhil) qualification (or be expected to have within 3 months of appointment) in substantially quantitative disciplines (e.g. mathematics, engineering, computer science, physics)
• Substantial experience of statistical or computational modelling gained within a research setting
• Experience of algorithmic development for statistical computing Expert proficiency in modern low- and high-level programming languages
• Ability to communicate complex information clearly
• Experience with the analysis of biomedical data (e.g. electronic health records, genomic data) would be advantageous.
• Ability to assess resource requirements and use resources effectively
• Understanding of and ability to contribute to broader management/administration processes

Closing Date 10 Jun 2019