Principal Investigator opportunities in Computational Biology
Opportunities for career development and established Principal Investigators
The Division of Computational Biology (CB) at the University of Dundee's School of Life Sciences (SLS) is seeking to appoint Principal Investigators (PIs) at all levels of seniority, from scientists aiming to start their first independent group to senior, established researchers who wish to extend and enhance their research programmes.
Ranked as the top University in the UK for Biological Sciences for the second consecutive UK Government Research Excellence Framework (REF), our Division is at the cutting edge of computational approaches and theoretical modelling in the Life Sciences. The Division envisions Computational Biology as a hub for bringing together computer science and artificial intelligence with physical science approaches to biology, as well as experimental biology and medicine. We provide a space where these disciplines converge to advance our understanding of biology and improve medical outcomes. Aligned with the university’s broader vision, we prioritize environmental sustainability and regard to societal and economic fairness in all our endeavours. We are particularly seeking researchers with backgrounds in bioinformatics, machine learning applied to biology, as well as sustainability and fairness in AI for biomedical data sciences, theoretical and computational biophysics, and biological physics. If you are passionate about computational biology and eager to make a significant impact, we invite you to join us in shaping the future of biological research.
New Principal Investigators will be welcomed into a diverse and vibrant community with expertise spanning software engineering and data management, cutting-edge machine learning technologies, and computational biological physics and biophysics. Computational tools and resources developed by our research groups are used daily by hundreds of thousands of researchers around the globe, supporting diverse areas of basic and applied research.
Our PIs also engage in wet-lab experimental work, by managing their own wet-labs, and/or partnering with laboratory-based groups or clinicians. These collaborations ensure that insights from experimental data are maximised and help in developing predictive algorithms and models.
Since 2014, the Division has occupied a purpose-built floor in the Discovery Centre building which is physically linked to the rest of the School of Life Sciences research complex. The Division benefits from access to a state-of-the-art, on-site, GPU-enabled High-Performance Computing cluster and data storage.
Successful candidates will be joining the Division at an exciting time of expansion providing the opportunity to contribute to our future direction. Career development Principal Investigators enter a Tenure Track Programme, including a defined mentoring scheme, leading to tenure assessment at 5-6 years. We are also keen to attract researchers with an aptitude for broader research leadership.
The Division is located within the School of Life Sciences which comprises around 70 research groups and nearly 800 staff and doctoral students from over 50 countries. The School's research income is around £45M per annum.
We welcome informal enquires and these can be directed to members of CB:
- Geoff Barton (bioinformatics/structural biology/machine learning)
- Hajk-George Drost (machine learning/bioinformatics/digital biology)
- Gabriele Schweikert (machine learning/bioinformatics/fairness in AI)
- Rastko Sknepnek (biophysics and biological physics)
The Division is proud of its positive research culture which provides an inclusive environment supporting all to flourish. We are staunch supporters of Open Research as signatories of DORA and are ranked 2nd in the World for our open-access research (CWTS Leiden Ranking 2023). We are committed to providing an inclusive and diverse environment. We particularly encourage applications from those who are currently underrepresented in research, including black and minority ethnic (BME) and those from socio-economically disadvantaged backgrounds.