PhD student position in Machine Learning

Project description
Machine learning is now an essential tool for scientists and engineers. It is used in diverse applications to predict outcomes from inputs by training models to minimize prediction error in training data. As widespread adoption reaches beyond research and the development of such systems, guarantees for accuracy, reliability and safety become critical. State-of-the-art models which achieve top accuracy on benchmark tasks routinely fail to generalize to new examples even in strongly related problem domains. In this project, we will study the intersection of causal inference and machine learning using auxiliary information to improve the sample-efficiency and domain generalization of learning algorithms.

Generalization in machine learning refers to a trained system performing well on previously unseen examples. When analyzing and developing learning algorithms, unseen examples are often assumed to follow the same distribution as training examples. However, real data often follows different patterns: 1) The distribution of testing (in-deployment) examples often differs from those collected for training, 2) We often have access to different information or variables at training time than we do when the trained system is deployed, 3) The number of samples is rarely as large as we would like it to be. One example of this is classification of medical images using deep neural networks, where training sets are often limited in size but rich in annotations which are not available when the trained system is used.

In this project, we will develop sample-efficient learning algorithms which makes the best possible use of small numbers of training examples by exploiting auxiliary information and causal analysis. We will apply these algorithms in, among other tasks, medical image classification.

Information about the division and the department
The Department of Computer Science and Engineering (CSE) is a joint department at Chalmers University of Technology and the University of Gothenburg, with activities on two campuses in the city of Gothenburg. The department has around 240 employees from over 30 countries and enrols a number of PhD students from more than 30 countries. Our research has a wide span, from theoretical foundations to applied systems development. We provide high quality education at Bachelor's, Master's and graduate levels, offering over 120 courses each year. We also have extensive national and international collaborations with academia, industry and society. The Data Science & AI division within CSE engages in research and education within a growing area encompassing Data Science, AI and Machine Learning.

The position is placed in the research group Healthy AI Lab led by Fredrik Johansson, currently comprised of 5 PhD students working on topics related to machine learning for improved decision making with applications in healthcare. The project is supported by Vetenskapsrådet Starting Grant 2022-04748: Kausalitet och sidoinformation för effektiv maskininlärning.

Major responsibilities
Your major responsibilities as a PhD student is to pursue your own doctoral studies. You will be enrolled in a graduate program in the Department of Computer Science and Engineering. You are expected to develop your own ideas and communicate scientific results orally as well as in written form. In addition, the position will include 20% departmental work, mostly teaching duties in Chalmers' undergraduate and masters-level courses or performing other duties corresponding to 20% of working hours.

Qualifications
To qualify as a PhD student, you must have a master's-level degree, or a four-year bachelor's degree, corresponding to at least 240 higher education credits in a relevant field. The position requires sound verbal and written communication skills in Swedish and English. If Swedish is not your native language, you should be able to teach in Swedish after two years. Chalmers offers Swedish courses.

Contract terms
Full-time temporary employment. The position is limited to a maximum of five years.

We offer
Chalmers offers a cultivating and inspiring working environment in the coastal city of Gothenburg. 
Read more about working at Chalmers and our benefits for employees.

Chalmers aims to actively improve our gender balance. We work broadly with equality projects, for example the GENIE Initiative on gender equality for excellence. Equality and diversity are substantial foundations in all activities at Chalmers.

Application procedure
The application should be marked with Ref 20230058 and written in English. The application should be sent electronically and be attached as PDF-files, as below. Maximum size for each file is 40 MB. Please note that the system does not support Zip files.

CV: (Please name the document: CV, Family name, Ref. number)
• CV
• Other, for example previous employments or leadership qualifications and positions of trust.
• Two references that we can contact.

Personal letter: (Please name the document as: Personal letter, Family name, Ref. number)
1-3 pages where you:
• Introduce yourself
• Describe your previous experience of relevance for the position (e.g. education, thesis work and, if applicable, any other research activities)
• Describe your future goals and future research focus

Other documents:
• Copies of bachelor and/or master’s thesis.
• Attested copies and transcripts of completed education, grades and other certificates, e.g. TOEFL test results.

Please use the button at the foot of the page to reach the application form. 

Application deadline: 01-03-2023

For questions, please contact:
Fredrik Johansson, CSE DSAI
E-mail: fredrik.johansson@chalmers.se
Phone: 0735917101

*** Chalmers declines to consider all offers of further announcement publishing or other types of support for the recruiting process in connection with this position. ***