Master thesis on Recommender Systems in collaboration with the Munich based Startup Venn
TL;DR: Build a recommender system using the latest ML techniques
- Take full ownership of the development of a recommender algorithm using real-life data
- Devise experiments to validate and compare the efficacy of your algorithms
- Work closely with technical founders to devise a scalable deployment strategy
- Set up tracking and analytics to improve the algorithm long term
About Introducing Venn!
We (Mark and Till) are two tech founders with a passion to build and deliver products.
We met and became close friends at one of Europe's leading AI startups. Being part of growing the company from 10 to 100+ employees, we know what it takes to scale teams successfully and build exciting products at a rapid pace. After three priceless years, we decided it's time to embark on a new adventure.
Our vision is to build an app that brings social recommendations online. We are currently in the pre-launch/prototyping phase and are aiming to launch our product by the end of September. Join us now and help Venn
to take off!
What you need to be successful
TL;DR: Expertise in ML, recommender algorithms, graph-based data structures
- Solid understanding of core ML concepts (activation functions, neural net architectures, optimizers, data splitting techniques etc.)
- Experience in building successful ML projects using TensorFlow?
- Ability to leverage the power of Cloud-based services
- Knowledge of graph-based data structures
- Theoretical and practical knowledge of state-of-the-art recommender algorithms
We will love your application, if...
TL;DR: You show exceptional technical ability, have a growth mindset, and are empathetic
- ... you are a student in TUM Informatics/Mathematics
- ... you have a proven track record of outstanding technical ability and knowledge
- ... you are deeply curious and have a strong drive to learn and improve
- ... you take responsibility and deliver above expectations
- ... you have the desire to learn how to deliver production-ready software
What we can offer you
TL;DR: Joining an early-stage startup with a close embedding in academia
- Have an impact from day one. We trust our team, so we will do our best to enable you to flourish
- Close supervision from the startup and the IBBM Research Group at TUM
- A super steep learning curve: you are learning from other highly motivated team members, our network, and rapid iterations
- A growth culture: instead of blaming each other, we focus on improvements; we risk things, analyze the results and learn for next time
- A fun team outside of work
- A Monetary compensation
- A Location preference for on-site in Munich, but remote is possible
Apply now at email@example.com
Supervisor: Johannes Paetzold
We are looking forward to hearing from you.