From personalized medicine research to data engineering, this journey at STACC was filled with learning, hands-on experience, and career growth. Read how a flexible internship led to diverse projects, from healthcare analytics to supply chain optimization.

From teaching English to leading data science projects – an unexpected career shift that became the perfect fit. STACC’s welcoming team and hands-on approach made the transition seamless, proving that new challenges lead to great opportunities.

How can AI support museum conservation? STACC collaborated with the National Heritage Board to develop a predictive model that assesses artifact stability, enabling museums to streamline inventory processes and focus on preserving the most at-risk objects.

How did AI step onto the stage? STACC’s data scientists developed an artificial intelligence capable of generating Chekhov-inspired plays, structuring plots, assigning characters, and even adding stage directions.

STACC’s team hacked their way through Junction 2019, developing an AI-powered smart lighting system using radar and gesture control. After two days of intense coding and minimal sleep, they successfully demoed their prototype – an experience full of challenges and fun!

STACC’s developers Carlos Bentes, Maksym Semikin and Meri Liis Treimann took part in the RecSys Challenge 2018, which was organized by Spotify, The University of Massachusetts, Amherst, and Johannes Kepler University, Linz, and concluded on June 30, 2018. Spotify is an online music streaming service with over 140 million active users and over 30 million tracks. One of

X-Road is a software framework that enables direct secure data exchange over internet between state and private organizations (hereafter “members”). It is the Estonian official software platform for data exchange between public and private sector institutions and enterprises. X-Road members can act as data clients or data providers or both. Data service providers make their

By Christian Safka, Data Scientist at STACC In this tutorial we will convert images to vectors, and test the quality of our vectors with cosine similarity. The full project can be found here. What is a feature vector? What I am calling a ‘feature vector’ is simply a list of numbers taken from the output