Search results (14)
« Back to Skills Training CalendarSystems immunology: an intro to multi-omics data integration and machine learning, online
Tuesday, 13 October 2026 to Wednesday, 14 October 2026, 10am - 4pm
Uses machine-learning approaches to integrate biological or biomedical multi-omics datasets for systems-level analysis and knowledge discovery.
Intro to R for biologists, online
Monday, 19 October 2026 to Wednesday, 21 October 2026, 9.30am - 1pm
Introduces the R programming environment for participants with little or no programming experience, covering data import, reorganisation, basic statistics and plotting.
Principles of data visualisation and big data handling, online
Monday, 19 October 2026 to Wednesday, 21 October 2026, 9.30am - 12pm
Covers selecting suitable visualisations, figure design, colour and storytelling with data, together with practical data-wrangling approaches for datasets too large for basic manual handling.
Introduction to machine learning and deep learning in Python, online
Tuesday, 20 October 2026 to Friday, 23 October 2026, 10am - 1pm
A practical introduction to preparing biological data and applying machine-learning and deep-learning methods in Python, including classification, regression, feature selection and convolutional neural networks for image data.
Image Analysis course with Fiji/ImageJ, online
Monday, 02 November 2026 to Friday, 06 November 2026, 9.30am - 12pm
Practical image-analysis training for researchers working with bright-field or fluorescence microscopy, using Fiji/ImageJ to process, quantify and interpret image data.
Data visualisation with ggplot2, online
Tuesday, 03 November 2026, 10am to 1pm
Develops existing R skills to produce clear, reproducible and publication-quality figures from biological or medical data using ggplot2.
Experimental design and statistics in preclinical research: the good, the bad and the ugly, in person
Tuesday, 03 November 2026 to Thursday, 05 November 2026, 2pm - 5pm
Examines the principles and common weaknesses of preclinical experimental design, with emphasis on appropriate statistical thinking, reproducibility and reducing bias.
Intro to R for biologists, online
Monday, 09 November 2026 to Wednesday, 11 November 2026, 9.30am - 1pm
Introduces the R programming environment for participants with little or no programming experience, covering data import, reorganisation, basic statistics and plotting.
Introduction to statistics, in person
Tuesday, 10 November 2026 to Monday, 30 November 2026, 1pm - 4.30pm
Introduces foundational statistical concepts used in medical and biological research and prepares participants to understand and use statistical software appropriately. No previous statistical knowledge is assumed.
Academic Writing with AI for postgraduate research students, online
Thursday, 12 November 2026 to Thursday, 03 December 2026, 2.30pm - 2.30pm
Academic Writing with AI provides a structured approach to integrate effectively AI tools at each stage of the writing process. This course is designed specifically for PGR.
Principles of data visualisation and big data handling, online
Wednesday, 25 November 2026 to Friday, 27 November 2026, 9.30am - 12pm
Covers selecting suitable visualisations, figure design, colour and storytelling with data, together with practical data-wrangling approaches for datasets too large for basic manual handling.
Image Analysis course with Fiji/ImageJ, online
Monday, 30 November 2026 to Friday, 04 December 2026, 9.30am - 12pm
Practical image-analysis training for researchers working with bright-field or fluorescence microscopy, using Fiji/ImageJ to process, quantify and interpret image data.
OxMic: Essentials of Light Microscopy, in person
Wednesday, 09 December 2026, 9.30am to 5pm
Provides a foundation in optical principles, fluorescence microscopy and the selection and use of microscopy techniques, with input from bioimaging specialists.
Experimental design and statistics in preclinical research: the good, the bad and the ugly, in person
Monday, 14 December 2026 to Tuesday, 15 December 2026, 2pm - 5pm
Examines the principles and common weaknesses of preclinical experimental design, with emphasis on appropriate statistical thinking, reproducibility and reducing bias.