The School of Life Sciences at the University of Dundee is a world-class academic institution with a reputation for the excellence of its research, its high quality teaching and student experience, and the strong impact of its activities outside academia. With 900 staff from over 40 countries worldwide the School provides a dynamic, multi-national, collegiate and diverse environment with state-of-the-art laboratory, technology and teaching facilities.

We are recruiting for an exceptional individual to join us as a PDRA within our Computational Biology Division. The successful individual will design, build and test Machine Learning models to elucidate the complex regulatory network in alternative splicing. Specifically, the project aims to investigate, model and predict the interactions between splice factors and their RNA substrates through a combination of interventional experiments and causal inference as well as explainable ML models. This is a fixed-term appointment for 12 months starting in March 2024, with the potential of extension subject to progress.

The project is funded by a BBSRC Pioneer Award. The postdoc will be part of an interdisciplinary team lead by Dr Gabriele Schweikert and Prof Angus Lamond. We have a unique combination of expertise that spans experimental approaches, large scale data analysis, applied bioinformatics and deep learning. The Lamond lab has a long track record of providing insights into the mechanisms of alternative splicing using a range of techniques, including genomics, proteomics and structural biology. The Computational Epigenomics Group, led by Dr Gabriele Schweikert is interested in understanding complex biological processes underpinning mechanisms of gene regulation by developing machine learning algorithms and applying them to genomic, epigenomic and transcriptomic data.

Your priorities will include:

• Compare state-of-the-art deep learning models for alterna3ve splicing predic3on


• Build a causal interpretable model for alterna3ve splicing


• Test model performance on simulated data


• Validate and update model on experimental, interventional data

Who we're looking for a candidate with:

• A Ph.D. in Computer Science, ML or a related field


• Understanding of Deep Learning Models with an interest in interpretability


• Basic knowledge of causal inference and discovery


• Experience working with sequencing data

We are one of the UK's leading universities, internationally recognised for our expertise across a range of disciplines and research breakthroughs in multiple areas, including science, medicine and engineering, amongst many others. Our purpose is to transform lives, locally and globally, which we do as a community of staff (Professional Services and academic Schools), students and alumni. Professional Services directorates are key to delivering the University strategy and driving change across the University.

For further information about this position please contact Dr Gabriele Schweikert, School of Life Sciences at G.Schweikert@dundee.ac.uk. To find out more about Group/Unit/Team/School please visit https://www.dundee.ac.uk/people/ gabriele-schweikert.

Commitment to DORA

The School of Life Sciences has been fully committed to the principals of the San Francisco Declaration on Research Assessment (DORA) since 2013. In assessing applicants, we consider the scientific quality of their published research papers, but do not take into account where the papers were published and do not consider journal-based metrics, such as Journal Impact Factors.