Open Positions


For the upcoming Machine Learning module (beginning September 2018), we might be hiring one or two demonstrators who help us in designing and checking coursework, i.e. programming assignments. If you have experience in machine learning, including machine learning frameworks such as scikit-learn, please contact us.

Go Abroad (to Tokyo)

We established a partnership with the National Institute of Informatics (NII) in Tokyo to offer SCSS’ and ADAPT’s master and PhD students paid research internships at the NII.

  • Who’s eligible: Master and PhD students at TCD’s school of computer science and statistics, and the students of the ADAPT Centre.
  • Duration: 3- 6 months
  • Application deadlines (twice per year):
  • Compensation (monthly): 171,000 JPY (approx. 1,300 Euro – sufficient to cover all living expenses)

More information is coming soon.

Visit from Abroad

If you are a student from Germany and would like to work with us for a few months, there are good chances to get a funded internship. For instance, DAAD FIT Weltweit allows German Master students to pursue a research project for some months at Trinity College Dublin. Applications are possible at any time, and success rates are high, too. Similarly, DAAD RISE Weltweit allows German Bachelor students to conduct a research project at TCD for up to three months during the summer.

If you are from some other country: most governments offer some fellowships for students to study and research abroad. Feel free to explore your country’s options, and contact us if you want to apply for a scholarship.

Self-Funded Internships

If you are self-funded and would like to work with us, we would love to hear from you.

FYP/Dissertation Supervision

If you are a student at TCD and would like us to supervise your FYP or dissertation, please check SCSS’ Student Projects website for a list of potential projects. We are also very open to hearing about your own project ideas. However, if you want to do one of the projects, or have your own idea, please read about how to continue in our WIKI (you need to register, and be signed in to read the WIKI, otherwise you will get a 404/dead-page error). Be advised to contact us as early as possible since we are only supervising a handful of projects each year. 


Machine Learning (CS7CS4)

Students who complete this module should be able to (1) Decide when machine learning is an appropriate method to solve a problem (2) Understand how machine learning works. (3) Apply machine-learning frameworks to solve real-world problems, and adjust and extend existing algorithms when necessary. The module content includes

  1. Machine Learning Basics (Application Areas, Challenges, Alternatives to Machine Learning)
  2. Machine Learning in Action (Datasets, Frameworks, Evaluation)
  3. Cross-validation and confidence intervals
  4. Overfitting/underfitting (bias-variance trade-off)
  5. Machine Learning Algorithms
    1. Linear Regression
    2. Logistic Regression
    3. Support Vector Machines
    4. Kernel Methods
    5. k-Means Clustering and Mixture Models for Unsupervised Learning
    6. Neural Networks
    7. Deep Learning Algorithms
  6. Use of gradient descent, and extensions for improved scalability (stochastic gradient descent etc)
  7. Probabilistic interpretations of ML algorithms.
  8. Maximum Likelihood and MAP estimators.
  9. Recommender systems

e-Business II (CS3BC2)

Students who complete this module will be able to analyse technical choices related to the design and platform selection for e-business application in relation to the business context and requirements. In addition, they will be able to design and program elements of web and mobile-based applications and web services to address an e-business problem. Module Content

  1. Evolution of the WWW as a business platform
  2. Web Application Servers and 3-tier enterprise architectures
  3. Web usage analytics and handling of personal data
  4. Web Services for e-business interoperability
  5. Workflow and web service composition for business-to-business applications
  6. Online social networks as business-to-consumer application platforms
  7. Enterprise Linked Data and the Semantic Web

Previous Lectures

Mobile Technologies

More details soon.

Data Handling

More details soon.

Programming & Modelling

More details soon.

IT Project Management

More details soon.