Industrial PhD - Göteborg, Sverige - Astrazeneca AB

Beskrivning

Are you ready to take the next step in your career and make a real impact in the field of data-driven life science? We are offering an exciting opportunity as an Industrial PhD student to work on a project that applies machine learning to improve diagnostic and reporting workflow processes in clinical kidney pathology.

This project is a collaboration between AstraZeneca, Royal Institute of Technology (KTH), and Karolinska Institutet, financed by Data-Driven Life Science (DDLS).

The successful candidate will also be part of the DDLS Research School.

You will be supported by Magnus Söderberg (Senior Director, Cardiovascular Renal Metabolism Pathology) at AstraZeneca as well as receive academic mentorship and guidance from Kevin Smith (Associate Professor) at KTH.

The position will be based at KTH in Stockholm, Sweden.

Accountabilities

As part of this role, you will be responsible for integrating multiple types of data, mainly histopathology digital images and diagnostic pathology text.

You will build models to automatically generate text from image data, and vice versa. You will explore optimal ways to integrate the generated models into regular clinical and industrial pathology workflows. You will also summarize findings in manuscripts to be presented at high impact international conferences and scientific journals.

Essential Requirements

Masters degree in a subject relevant to the project

A strong background in mathematics

Knowledge of machine learning

Experience with natural language processing (NLP) and computer vision (CV)

Ability to work in a Linux console environment

Proficiency in PyTorch, TensorFlow, or Jax

To be successful in this role, it is of key importance to demonstrate a high level of independence in the pursuit of your work.

You need to have excellent collaborative skills, a highly professional approach and also well-developed abilities to analyse and work with complex issues.

Desired Qualifications

Experience of application of machine learning in a life science context

So what's next?

We welcome your application no later than May We will review applications on a regular basis so please apply as soon as possible.

Are you ready to join a team that's pushing the boundaries of science to deliver life-changing medicines? If your passion is science and you want to be part of a team that makes a bigger impact on patients' lives, then there's no better place to be.

Apply today