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1 Year On Campus Masters Program
Queen Mary University of LondonThe MSc Artificial Intelligence in the Biosciences at Queen Mary University of London trains students to apply machine learning, data science and computational methods to biological and biomedical data. It suits graduates from biology, biochemistry, life sciences or related fields who want to use AI to solve complex bioscience, biomedical or ecological problems.
Curriculum Structure:
Students begin with Coding for Bioscientists and Statistics for Biologists, building essential programming and quantitative skills for handling biological datasets. They then take AI and Data Science in Biology, learning to apply machine-learning tools to genomic, molecular and omics data. As the year progresses, they choose modules such as AI and Data Analytics in Physiology and Biomedicine or AI and Data Analytics in Ecology and Evolution, applying computational techniques to real biological systems. The programme ends with an AI in the Biosciences Research Project, where students conduct an independent investigation using AI to analyse a complex biological or biomedical dataset.
Focus areas: “Bioinformatics, Machine Learning for Biology, Genomic Data Analysis, Biomedical AI, Ecological Data Analytics”
Learning outcomes: “Code and analyse biological datasets; apply ML to genomic and molecular data; interpret large-scale bioscience datasets; carry out independent AI-driven bioscience research.”
Professional alignment (accreditation): Designed to meet growing demand in biotech, biomedical research, healthcare analytics and environmental data science for professionals with strong biological and AI skills.
Reputation (employability rankings): QMUL’s strong research environment and Russell Group status support competitive employability in bioinformatics, biotech, healthcare data and environmental research sectors.
Students gain practical skills by applying AI and machine learning to biological and biomedical datasets, using QMUL's computational infrastructure and working with research groups across biological sciences. This applied learning is central to the curriculum:
Software: Training in Python and R with key AI and bioinformatics libraries (TensorFlow, Scikit-learn, Biopython).
Computing Facilities: Access to QMUL's High-Performance Computing (HPC) resources.
Bioscience Projects: Hands-on work with genomic, proteomic, and imaging data.
Research Project: An individual dissertation in AI applications for biosciences.
Interdisciplinary Focus: Combines AI with molecular biology and biotechnology.
Research Links: Connections to the School of Biological and Behavioural Sciences research groups.
Graduates of Queen Mary University of London's MSc Artificial Intelligence in the Biosciences master machine learning, data analysis, coding, and statistical methods to apply AI in biological research, bioinformatics, ecology, and biomedicine. Hands-on projects using the Apocrita supercomputer prepare alumni for R&D leadership amid surging demand for AI-skilled bioscientists. Typical job roles: data scientist, bioinformatician, AI specialist, research scientist.
University services: Careers service provides CV support, interview training, networking with London AI/pharma employers.
Employment stats/salary: 80-84% graduate prospects; high demand in biotech/healthcare with competitive salaries.
University–industry partnerships: Alan Turing Institute member; Digital Environment Research Institute collaborations.
Long-term accreditation value: REF top-10 research impact ensures expertise in growing AI-biosciences field.
Graduation outcomes: Roles in pharma, biotech, NHS, consultancy tackling health/sustainability challenges.
Further Academic Progression: Pursue PhD via Queen Mary's Turing Institute-linked programmes, extending biosciences AI research.
A 2:1 or above at undergraduate level in a Science subject such as Biology, Chemistry, Mathematics or a related subject.USA- UK 2:1 degree: GPA 3.2 out of 4.0India - Bachelor Degree (minimum 3 years) from selected institutions. UK 2:1 degree: 60% to 70%IELTS (Academic) minimum score 6.5 overall with 6.0 in each of Writing, Listening, Reading and SpeakingTOEFL minimum score 92 overall with 21 in Writing, 19 in Reading, 18 in Listening and 21 in SpeakingBioinformatics ScientistComputational BiologistBiomedical AI SpecialistGenomic Data ScientistPharmaceutical AI ResearcherBiotech Data AnalystClinical Bioinformatics OfficerResearch Scientist (AI-bio)Healthcare AI DeveloperPrecision Medicine SpecialistDrug Discovery AnalystBiological Imaging Analyst
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