Recherche

Postdoc fellowship in machine learning and disease prediction

Are you interested in a Postdoc Fellowship, fully funded by the Swiss National Science Foundation (SNF) and in Professor Oliver Y. Chén's team? In this case, keep reading! 👇

Contexte

The Lausanne University Hospital (CHUV) is one of Switzerland’s five university hospitals. Thanks to its collaboration with the Faculty of Biology and Medicine of the University of Lausanne and EPFL, the CHUV plays a leading role in medical care, medical research, and education.

You will journey with us on the following adventures:

a) Building new statistical/machine learning methods for studying large-scale, potentially high-dimensional and multimodal data

b) Disease prediction

c) Machine learning-based biomarker discovery.

The Postdoc Fellow will have joint affiliations with the Lausanne University Hospital (CHUV) and the University of Lausanne.

What does our group do ?

We develop new machine-learning and statistical methods and study large-scale data in health and disease. Our data are from diverse sources, from brain imaging (e.g., MRI and EEG), sequencing, mass cytometry/spectrometry, and health records, to data from digital devices such as smartphones.

Our focus is threefold:

a) Building new, methodologically exciting models to address real-world problems

b) Using these methods to study the interplays between large-scale multimodal, multivariate, high-dimensional features, and when/how they may be associated with diseases cross-sectionally and longitudinally and identify markers that support patient diagnosis and prognosis

c) Translating our algorithms into clinical decision support and patient health management apps.

Mission

With this Postdoc Fellowship, you will primarily work on these aims :

• Developing new machine learning methods to improve the identification of patients with brain illnesses, such as Alzheimer’s disease (AD). Designing new machine learning methods to identify patients using large-scale, potentially high-dimensional multimodal data
• Discovering biomarkers predictive of disease severity. Developing efficient methods to discover biomarkers, including graph biomarkers from single- or multi-modal data
• Building software for conducting automated biomarker selection and disease prediction via machine learning. Developing software, toolboxes, and apps that can improve the efficiency of biomarker discovery and disease prediction by leveraging insights from (1) and (2).

You will have the freedom to propose and develop independent studies or join other projects within the broader aims of this scholarship and collaborate with or visit other teams.

Furthermore, you will work in an interdisciplinary and multicultural environment.

Profil

Minimum qualifications:

• PhD (or successful PhD defense before starting the position), ideally with experience in machine learning, preferably in graph theory and brain science
• Interest in developing new methods and applications to address real-world problems
• Strong interest in data visualization
• Proven publication track record
• Fluency in English
• Team player with a passion for interdisciplinary collaboration.

Desired qualifications:

• Strong programming skills in MATLAB, R, and/or Python
• Experience in machine learning (including graph theory), statistical modeling, and version control.

Nous offrons

What do we offer ?

• A Postdoc Fellowship sponsored by the SNF
• Joint affiliations with the Lausanne University Hospital (CHUV) and the University of Lausanne
• An interdisciplinary environment, and a supportive team. We strive for equality, diversity, and inclusion. Our team is interdisciplinary and multicultural, and we encourage underrepresented groups to apply
• Possibility to collaborate with and visit external colleagues at F. Hoffmann-La Roche, Johns Hopkins University, KU Leuven, University of Bristol, University of Oxford, University of Pennsylvania, Vrije Universiteit Brussel, and Yale University
• Access to courses from the CHUV and the University of Lausanne.

To become an employee of the world-famous University Hospital Center from the Canton of Vaud is an assurance of

• First-rate social benefits such as a Paternity Leave of 20 days and a Maternity Leave of 4 months (there is also the possibility to obtain a complementary breastfeeding leave of 1 month)
• Regular salary progression adapted to your responsibilities
• 25 days of vacations per year
• A right to at least three days of training per year, by accessing a wide offer of courses not only from the CHUV Training Center but also from external providers
• Possibility to access one of the 500 furnished apartments offered in the surrounding neighborhoods in case of relocation in Switzerland
• Discounts proposed on social and cultural events, goodies and other services, thanks to the “H-Oxygène” association
• Signing up to our Mobility Plan and benefit from different advantages (discounts on public transportation, promotion of “Mobility” car fleet and discounts on electric bikes)
• Being able to enjoy our high-quality corporate restaurants, located in every hospital building, with employees’ discount.

Contact et envoi de candidature

Contact person in case of questions about this role : Professor Oliver Y. Chén, Head of Unit, Bioinformatician, phone 021 314 16 11

All of our applications are processed electronically. For this reason, we kindly ask you to apply exclusively by clicking on the APPLY button at the bottom of the advertisement.

Should you experience any problems with your application, you can consult our document "how to apply online". In case of technical issues, you can contact our Recruitment team who will help you (E-Mail schreiben / +41 21 314 85 70)

The CHUV applies the highest quality requirements as part of its recruitment process. In addition, mindful to promote workplace diversity and inclusion we strive to ensure equal treatment and avoid any discrimination. We are looking forward to receiving your application.

We would like to inform external recruitment agencies that any application inserted directly on our recruitment platform won’t be accepted and cannot be charged. Thank you for your understanding.