Artificial Intelligence for Life Sciences MSc

Core modules:

Neural Dynamics and Computation
Scientific programming in computational and cognitive neuroscience

Research project in artificial intelligence for life sciences

This module will give you the opportunity to develop advanced skills relevant to careers applying AI to problems in life sciences. 

Projects will cover a range of topics across the life sciences, reflecting the diverse applications of AI. You'll carry out a desk-based computational project, either independently or as part of a small team contributing to a larger research challenge, potentially in collaboration with an external organisation.

All projects involve independently planning and producing an original piece of research, under the guidance of an academic supervisor. You'll then tailor your final write-up to a specific audience - either as a scientific paper, a review article, or a sector-specific report.

60 credits

Data Management and Visualisation

This module will develop your basic skills in computational data analysis. 

You'll learn how to import and export scientific data sets in different formats, how to process and transform them, and how to visualise results.

Teaching will focus on the programming language R and associated scientific software, built around hands-on, computer-based opportunities to hone your skills. No prior programming experience will be necessary.

15 credits

Ethics of Artificial Intelligence and Contemporary Technology

This module will introduce you to a range of contemporary and emerging ethical questions linked to developments in AI and AI-powered technologies.

Topics are updated in tandem with new developments in AI, but could include decision-making dilemmas and responsibility attribution concerns for lethal autonomous weapon systems, relationships with chatbots and robots, challenges for AI value alignment, algorithmic manipulation and injustice, and questions surrounding cyborgism and being human in the age of AI.

The module will focus on equipping you with skills to undertake ethical analysis of current and emerging AI use cases, and improve your understanding of how philosophical theories can be used to tackle practical real-world challenges in the fast-paced world of AI and robotics.

15 credits

Applied Statistical and Machine Learning Methods for Empirical Research

This module will introduce you to statistical techniques increasingly used in empirical research, including generalised and multilevel models, structural equation modelling and machine learning approaches. 

We'll focus on understanding the rationale and application of these methods, with lectures focusing on core concepts and principles, and practical computer-based sessions providing hands-on experience using relevant languages (e.g. Python, R).

15 credits

AI Applications in Biosciences

This module explores cutting-edge applications of artificial intelligence that are revolutionising biosciences. 

You'll gain hands-on experience in using AI tools and techniques to analyse biological data, model complex systems, and accelerate scientific discovery. 

By using practical examples and case studies, we'll show you how AI is transforming various aspects of biosciences. Biological and cross-cutting areas that we'll cover could include cell and molecular biology; physiology, development and disease; biodiversity science; machine learning; computer vision; and natural language processing in biosciences.

You'll get hands-on experience applying what you've learnt to topics such as predicting biological structures and automatically classifying images or audio recordings, skills that are increasingly required by employers in the biosciences.

15 credits

AI-Augmented Scientific Discovery: Frontiers and Issues

This module explores the current frontiers of AI and how we can apply it to answer scientific questions.

You'll take part in workshops and seminars led by researchers across the life sciences, examining how AI methods are transforming their research, their impact, and shaping employment opportunities. 

Example topics that could be covered include: predicting protein structures from amino acid sequences; predicting the medical or molecular consequences of nucleic acid mutations; analysis of microscopy or medical images; automatic detection and monitoring of biodiversity from images or acoustic data; analysis of behavioural data; natural language processing for text analysis; neuroimaging analysis; and connections between machine learning methods and statistics. 

An important part of this module will include critiquing the technical and ethical issues raised by AI applications. Here you'll discover the impact of emerging issues such as data bias, model interpretability, reproducibility, and environmental impact. You'll also understand how researchers are using AI for more generic tasks such as systematic searching of the literature, and use as a coding assistant.

30 credits