Research Associate - School of Biosciences - 105566 - Grade 6
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Research Associate - School of Biosciences - 105566 - Grade 6
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Job Description
Position Details
School of Biosciences
Location: University of Birmingham, Edgbaston, Birmingham UK
Full time starting salary is normally in the range £32,546 to £35,116 with potential progression once in post to £39,355
Grade: 6
Full Time, Fixed Term contract up to May 2026
Closing date: 6th May 2025
Background
The Research Assistant role will develop computational pipelines and perform data analysis for the EU Partnership for the Assessment of Risks from Chemicals (PARC) project, and other associated projects, activities and research.
PARC aims to develop next-generation chemical risk assessment to protect human health and the environment. It supports the European Union's Chemicals Strategy for Sustainability and the European Green Deal's “Zero pollution” ambition with new data, knowledge, methods and tools, expertise and networks.
This role will collaborate with Principal Investigators, postdoctoral researchers, and PhD students on the EU PARC and related projects. The primary responsibility of this position is to develop comprehensive machine learning and artificial intelligence (AI) pipelines for single- and multi-omics modelling and prediction. Working under PI supervision, the role will focus on (but not limited to) implementing graph neural networks, network biology approaches, multimodal learning techniques, and explainable AI methodologies.
Role Summary
The post holder will perform data pre-processing and analysis using both the developed computational pipelines and existing bioinformatics and biostatistics tools for the EU PARC and associated projects. The Research Assistant will be responsible for summarising computational results and scientific findings, and contributing to relevant research publications. In addition, the position involves assisting the PIs to provide supervision to Master's-level research projects related to the core research areas.
Furthermore, the Research Assistant will actively participate in regular team meetings and present findings. The role requires maintaining comprehensive documentation of all code, analytical methods, and procedures to ensure reproducibility and knowledge transfer. The post holder will assist with project management aspects including tracking milestones, managing timelines, and contributing to technical reports for the EU PARC project. Additionally, the Research Assistant will be responsible for implementing robust data management practices, ensuring proper organisation and storage of research data while maintaining compliance with relevant data protection regulations and institutional policies.
Main Duties
* Develop and implement machine learning and AI pipelines for single- and multi-omics modelling and prediction, with emphasis on graph neural networks, network biology, multimodal learning, and explainable AI approaches.
* Conduct data pre-processing, integration, and analysis using developed pipelines and established bioinformatics and biostatistics tools for the EU PARC and related projects.
* When needed, collect additional research data from public databases and existing publications to support omics analyses.
* Summarise computational results and scientific findings in a clear, accessible format as directed by Principal Investigators.
* Present research outputs through various channels, including drafting sections of scientific publications and delivering presentations at team meetings.
* Maintain comprehensive documentation of all code, analytical methods, and procedures to ensure reproducibility and knowledge transfer.
* Assist with project management aspects including tracking milestones, managing timelines, and contributing to technical reports.
* Implement robust data management practices, ensuring proper organisation and storage of research data while maintaining compliance with relevant data protection regulations.
* Provide guidance and supervision to Master's-level students on research projects related to the core research areas.
* Promote equality and value diversity, acting as a role model and fostering an inclusive working culture.
Person Specification
* Received a Master’s degree or equivalent in bioinformatics, computational biology, or similar subject areas.
* Proficiency in data science, machine learning, and AI, particularly in graph neural networks, network biology, explainable AI, and multimodal learning.
* Experience in applying data science, machine learning, and AI techniques to analyse both single- and multi-omics data, with a focus on transcriptomics and metabolomics.
* Skills in Python and R programming, with knowledge of established bioinformatics, biostatistics, data sciences, machine learning and AI packages.
* Ability to manage scientific resources, e.g. data, source codes, and result files, and access public resources.
* Ability to manage research project and communicate effectively.
* Knowledge of the protected characteristics of the Equality Act 2010, and how to actively ensure in day to day activity in own area that those with protected characteristics are treated equally and fairly.
Informal enquiries to Professor Luisa Orsin, email: l.orsini@bham.ac.uk and Dr Jiarui Zhou, email: j.zhou.3@bham.ac.uk
We believe there is no such thing as a 'typical' member of University of Birmingham staff and that diversity in its many forms is a strength that underpins the exchange of ideas, innovation and debate at the heart of University life. We are committed to proactively addressing the barriers experienced by some groups in our community and are proud to hold Athena SWAN, Race Equality Charter and Disability Confident accreditations. We have an Equality Diversity and Inclusion Centre that focuses on continuously improving the University as a fair and inclusive place to work where everyone has the opportunity to succeed. We are also committed to sustainability, which is a key part of our strategy. You can find out more about our work to create a fairer university for everyone on our website .
Seniority level
* Seniority level
Entry level
Employment type
* Employment type
Full-time
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Research and Other
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Higher Education
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