Address of the bookmark: http://www.biotnet.org/training-materials/python-programmers

The Department is at the forefront of developments in machine learning, data science, and computational methods for understanding complex biological systems, integrating these approaches into both its research and teaching. This role offers significant scope to shape the future of bioinformatics and quantitative biology at Oxford and beyond.

You will be expected to develop and lead a world-class research programme in bioinformatics, helping to strengthen Oxford's position in areas such as statistical genomics, computational biology, machine learning for biological data, engineering biology, systems biology, and biomedical data science. We value the breadth of approaches that contribute to modern bioinformatics. Whatever your particular focus, you will understand how quantitative and computational methods can be applied to important biological and biomedical questions, and how collaboration across disciplines can generate new insights and opportunities. Alongside your own research, you will support the development of doctoral students, postdoctoral researchers, and early-career academics. You will contribute to our teaching at undergraduate and postgraduate level, delivering lectures, supervising student projects, and supporting graduate research students.

We are looking for someone who combines scholarly distinction with a collaborative and ambitious approach to research. You will be a researcher with an international reputation in bioinformatics, with a strong record of producing influential research at the interface of statistics, computational biology, genomics, and data science, and of securing research funding. Your work should advance the field, whether through methodological innovation, biological discovery, or the development of new approaches to understanding complex biological and biomedical data. You will bring experience of shaping research strategy, building productive collaborations, and exercising academic leadership. Above all, we wish to appoint someone who shares our values of intellectual curiosity, academic excellence, scholarly integrity, and a commitment to fostering a positive, supportive, and inclusive environment for all.

If you would like to discuss this post, please contact the Head of the Department of Statistics, Professor Frank Windmeijer, frank.windmeijer@stats.ox.ac.uk, or for information about University College please contact the Senior Tutor, Dr Andrew Bell, andrew.bell@univ.ox.ac.uk. All enquiries will be treated in strict confidence and will not form part of the selection decision.

The closing date for applications is 12:00 noon UK time on Monday 21 September 2026. Interviews are expected to be held in November 2026.

Apply here https://my.corehr.com/pls/uoxrecruit/erq_jobspec_version_4.display_form

http://www.csir.res.in/External/Heads/aboutcsir/announcements/ProjectPost_130813.pdf

To attempt to answer these questions about the basics of how evolution works, we use a combination of computational biology, analytical theory, comparative genomics.

More at https://yeamanlab.weebly.com

This project presents demonstrations of selected computer science algorithms important in bioinformatics, implemented in the spreadsheet program Microsoft Excel. Spreadsheets provide an interesting platform for demonstration of algorithms, since various steps of the calculations can be exposed in a manner that is easily comprehensible to users with little programming experience. The algorithms demonstrated include two approaches to approximate string matching (dynamic programming and Shift-AND numeric approximate matching), Hierarchical Clustering (used in phylogenetic studies and microarray analysis of gene expression), a Naive Bayes Classifier for simulated microarray gene expression data, and a simple Neural Network. These demonstrations are designed to serve as instructional aids in bioinformatics courses.

Tutorial @ http://www.cybertory.org/downloads/bae/BioinformaticsAlgorithmsInExcel.zip

One of the best resource for online bioinformatics learning is https://stepic.org/Bioinformatics-Algorithms-2 Enjoy the online learning.

Reference : cybertory

" Please add your favourite bioinformatics algorithms and tutorial links below in the comment section, for the benefit of bioinformatics and computational biology community ". 

Address of the bookmark: http://www.cybertory.org/downloads/bae/BioinformaticsAlgorithmsExcelDoc.pdf

http://elements.eaglegenomics.com/

4273π is a means to teach bioinformatics, including systems administration tasks, to undergraduates at low cost.

Descriptive paper @ http://www.biomedcentral.com/1471-2105/14/243

Image source: BMC Bioinformatics

http://www.isb-sib.ch/education/training-courses.html

Canadian bioinformatics also organises various bioinformatics and sequencing courses:

http://bioinformatics.ca/workshops

In addition to above two, EMBI Europe, EMBO Europe, Cold Spring Harbour USA, Wellcome Trust UK and NOVA Europe also organise bioinformatics and sequencing courses annually:

http://www.embl.de/training/events/index.php?p_outstation=ALL

http://www.embo.org/funding-awards/courses-workshops

http://meetings.cshl.edu/courses.html

http://www.wellcome.ac.uk/Education-resources/Courses-and-conferences/Advanced-Courses-and-Scientific-Conferences/Advanced-Courses/index.htm

http://www.nova-university.org/pagetop.cfm?MenySidorTop_id=2&open=7

Future projects and goals

Large-scale systematic maps of gene-gene and gene-environment interactions by automated phenotyping, using image analysis, machine learning, sparse model building and causal inference.
DNA-, RNA- and ChIP-Seq and their applications to gene expression regulation: statistical and computational foundations.
Cancer genomics, genomes as biomarkers, cancer phylogeny.
Image analysis for systems biology: measuring the dynamics of cell cycle and of cell migration of individual cells under normal conditions and many different perturbations (RNAi, drugs).

More @ http://www.embl.de/research/units/genome_biology/huber/index.html

Uni Computing (formerly Uni BCCS) is a department of Uni Research, affiliated with the University of Bergen.

5 groups in this lab works on computational resources, methods, algorithms, and software.

Following two bioinformatics groups are:

The Computational Biology Unit (CBU) provides education and research in bioinformatics focused on functional genomics.

The Computational Ecology Unit (CEU) is basically deal with population fluctuations, behavioural patterns and the ways life cycles emerge.