Scientist in Biolog

Start date: As soon as possible

End date: 30/09/2025, possible extension

Responsibilities:

• Playing a leading role in developing in silico experimental design and operation of a data-driven research group
• Undertake rigorous advanced analytics exploration of data from disease areas of strategic relevance
• Reproducible reporting of emergent biological insights to stakeholders
• Facilitating access to enterprise data and analysis tools for non-data scientists
• Performing, customising, and/or developing computational analyses/algorithms for raw data from sequencing-based assays, such as whole genome/exome sequencing, single-cell and bulk RNA-seq, and other data types (e.g. proteomics, genome-wide phenotypic screens)
• Pre-processing of raw datasets, high-level analysis, and visualisation to enable interpretation and deduce new biological insights • Analyse large-scale datasets in collaboration with members of the group and internal collaborators
• Validation and optimisation of emerging computational research tools
• Facilitating and supporting research collaborations with experimental scientists
• Tool and application development and maintenance (e.g. Shiny)
• Development and implementation of novel and existing standardised workflows and pipelines
• Data management, standardised reporting, and maximising democratised access to data and analytics consistent with FAIR principles
• Contribute to creation and revision of peer development curriculum for workshops and small-group training
• Ability to work as part of matrix teams, taking leadership as well as participant roles as needed to ensure the success of individual teams and the organisation. Experience building and maintaining stakeholder relationships across an organisation
• Ability to analyse and integrate a broad range of complex scientific information into higher level understanding and novel concepts, then translate those concepts into actionable concrete outputs and testable hypotheses

Requirement:

• PhD in a relevant discipline
• Minimum of 5 years of postdoctoral experience, which may include professional experience within the industry
• Programming/scripting skills in R and/or Python is required.
• Working knowledge of Linux/Unix, with experience in data processing in an HPC cluster environment and basic understanding of computer systems administration
• Experience of working alongside experimental ‘wet-lab’ scientists, and managing several concurrent projects with changing priorities
• Strong statistical, reporting, and data visualisation skills. Data, code, and project hygiene are essential
• A strong genetics foundation including experience with Mendelian Randomisation, GWAS, and integrative analysis e.g. proteogenomic modelling, is expected
• Expertise in graph theory and application of network-based approaches to biological data including building disease models, multi-layer graph models, graph search tools and graph representational learning, Bayesian and/or logic networks
• Expertise in the integration and visualisation of multimodal data from human clinical and/or model systems
• Algorithm development, data mining, and statistical analysis of large datasets (including approaches such as Bayesian statistics, Markov models, simulation models, graph/network exploration methods, and machine learning)
• Experience in handling, integration, and visualisation of multimodal data (e.g. CITE-seq and spatial transcriptomics data) and/or data from proteomics or metabolomics studies