Master's Thesis Project
Recommender systems play a valuable role in the field of cybersecurity by providing personalized recommendations and insights to users based on their security preferences and interests.
In this project, you will work closely with our team to develop and test advanced recommender system techniques that improve the security of computer networks. You will work on developing and testing novel machine learning algorithms and data analysis techniques to improve the effectiveness and robustness of recommender systems in cybersecurity applications.
Our team is composed of experienced professionals with advanced degrees in machine learning, cybersecurity, and related fields, including PhD and Master's graduates from top universities. We are committed to advancing the state of the art in these fields and providing our students with a supportive and collaborative research environment.
Working on this project will provide you with a unique opportunity to gain hands-on experience in cutting-edge research at the intersection of machine learning and cybersecurity. You will have the chance to work with experienced professionals in the field, contribute to important research, and develop valuable skills for your future career.
You will have the opportunity to gain hands-on experience with state-of-the-art tools and technologies, including Python, Scikit-learn, deep learning (TensorFlow) and graph analysis. We require a knowledge of Python and machine learning, particularly recommender systems and basic experience with Scikit-learn and TensorFlow. Experience in data preprocessing/visualization and knowledge of graph analysis will be a plus.
- Experience with Python
- Applicants must be completing a thesis for a Masters degree
- Experience with machine learning
- Full time availability
- Applicants must be located within Denmark
This is a full time project which will take place over 5-6 months and is mainly a work from home position. You will have the full support and mentorship of our Research Specialist and our network of professors but if you are already working with a professor or supervisor you are more than welcome to continue with them if you prefer.
We are looking to start in the Autumn semester but we can be flexible depending on your schedule.