Rasmus Johns

Rasmus Johns has a diverse work experience spanning several industries. Rasmus is currently employed at Abios as a Team Lead, where they lead a team and works on extracting statistics from live video streams using computer vision models. Prior to that, Rasmus worked as a Data Scientist at Abios, where they contributed to the computer vision team's efforts.

Before joining Abios, Rasmus worked at Opera Software AS as a Data Analyst. In this role, they collaborated with stakeholders at all levels to identify and solve problems. Their responsibilities included building dashboards, using machine learning (especially NLP), discussing strategy, and conducting exploratory analytics.

Rasmus also gained experience as a Master's Thesis student at the Swedish Defence Research Agency, where they explored deep reinforcement learning in a cooperative multiagent environment. At Ericsson, they worked as a Researcher during a summer internship, implementing dynamic TDD in a simulated environment.

Additionally, Rasmus had roles as a software engineer at Sectra, a blogger at Linköping University, an administrator at Anders Risling AB, and a substitute teacher at NGS Vikariepoolen. Rasmus also worked as a Laboratory Assistant at Karolinska Institutet, conducting malaria research and performing various tests.

Throughout their career, Rasmus has demonstrated their skills in data analysis, machine learning, software engineering, and research. Rasmus has experience in various industries, including technology, research, education, and healthcare.

Rasmus Johns completed their Master of Science in Engineering (MSE) in Computer Science at Linköping University from 2013 to 2018. Prior to that, they attended Stockholm University from 2012 to 2013, where they studied Academic and Scientific Writing in English and Programming Techniques. In addition to their formal education, Rasmus has obtained several certifications. These include "Distributed Computing with Spark SQL" from Coursera, obtained in April 2022, "Sequences, Time Series and Prediction" obtained in June 2020, "Convolutional Neural Networks in TensorFlow" obtained in May 2020, "How to Win a Data Science Competition: Learn from Top Kagglers" obtained in April 2020, "Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning" obtained in April 2020, "Neural Networks and Deep Learning" obtained in February 2020, and "Introduction to Data Science in Python" obtained in November 2018.