PhD student Machine Learning i Stockholm

Klarna’s mission is to free people from all the meaningless time spent managing money and purchases, so they can do more of what they love. Every day at Klarna we help consumers, merchants, and partners to explore just how smoooth the modern purchase experience can be. Our position at the crossroads of payments, consumer financing, ecommerce and banking means we are uniquely positioned to do this. There is no label for what we do.

Klarna was born in Stockholm in 2005 and today has 2500 employees working across Europe and the US. We currently serve 80 million consumers, work together with 190,000 merchants and process more than a million purchases a day. We're growing at 40% every year and our investors include Visa, Atomico, Sequoia Capital, Permira and Bestseller group/ Anders Holch Povlsen. We have strong partnerships with some of the world’s leading brands, such as ASOS, IKEA, Adidas, Zara, Lufthansa and Spotify.

KTH Royal Institute of Technology in Stockholm has grown to become one of Europe's leading technical and engineering universities, as well as a key centre of intellectual talent and innovation. It is Sweden's largest technical research and learning institution and home to students, researchers and faculty from around the world. The research and education covers a wide area including natural sciences and all branches of engineering, as well as architecture, industrial management, urban planning, history and philosophy.

You will be working in the Web Automation Research team on the problem of automating web workflows, specifically leveraging methods in Deep Reinforcement Learning. This problem is concerned with devising an intelligent agent that learns to complete complex tasks at previously unseen websites. The ultimate goal is to allow this agent to complete complex tasks on behalf of the user, such as booking a flight or making a dinner reservation. Research areas you will likely come into contact with include: reinforcement learning, graphical models, neural networks, time-series analysis etc. You will tackle these open problems using resources often unavailable in academia, such as data collection pipelines, substantial cloud computing power, help from experienced software engineers and researchers; while also having the support and supervision from KTH. We are looking for driven and ambitious candidates who aim to publish in top-tier conferences such as NeurIPS, ICML, ICLR, TheWebConf or SIGKDD. You should expect and be comfortable with high demands and have a positive, can-do attitude.

Requirements:

MSc degree in one of the quantitative sciences: Computer Science, Applied Statistics, Machine Learning, Physics, Mathematics etc.

Comfortable with mathematics, in particular probability, and programming in Python.