PhD student in the clinical chemistry Service

Stagiaires, étudiant-e-s, apprenti-e-s

PhD student in the clinical chemistry Service

The clinical chemistry service is looking for a PhD student in machine learning, multimodal and high-dimensional data analysis.

We invite highly motivated candidates to join Dre Nazanin Sédille (https://www.chuv.ch/fr/laboratoires/dl-home/le-departement-en-bref/unites/service-de-chimie-clinique) and Professor Oliver Y. Chén’s teams (www.oliverychen.com). They work on projects related to: (a) building new machine learning and statistical methods for studying large-scale biological and medical data; (b) disease prediction; (c) digital health. For this PhD scholarship in particular, please see details below. The student will have joint affiliations with the Lausanne University Hospital (CHUV) and the University of Lausanne.

Contexte

The Lausanne University Hospital (CHUV) is one of five Swiss university hospitals. Through its collaboration with the Faculty of Biology and Medicine of the University of Lausanne and the EPFL, CHUV plays a leading role in the areas of medical care, medical research and training.

Dre Nazanin Sédille and Professor Oliver Y. Chén's teams develop new machine-learning and statistical methods and study large-scale data in health and disease. Our data are from diverse sources from the Service of Clinical Chemistry (https://www.chuv.ch/fr/laboratoires/dl-home/le-departement-en-bref/unites/service-de-chimie-clinique).

Their focus is threefold. (a) Building new, methodologically exciting models to address real-world problems; (b) using these methods to (i) 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 (ii) identify markers that support patient diagnosis and prognosis; (c) translating our algorithms into clinical decision support and patient health management apps.

Mission

The PhD student will primarily work on three interlinked projects in collaboration between CHUV and UNIL on data related to proteomic and metabolomic profilings in immunological and haematological diseases.

1. Automated biomarker discovery and disease prediction. We design machine learning methods to identify biomarkers associated with and predictive of disease outcomes (disease status, disease severity/clinical score, and longitudinal disease progression/fluctuation) in an automated way.

2. Discovering latent biomarkers to improve disease prediction and biological explanation. We design machine learning frameworks to identify, from multivariate, multimodal, and potentially high-dimensional data for latent, potentially nonlinear biomarkers that could better explain and predict the disease. We have data from metabolic diseases but the methods can be generalised to other diseases.

3. Longitudinal, personalised disease prediction. In addition to discovering population-level biomarkers (that are shared across subjects), we design methodological frameworks that incorporate, yet, distinguish, population-level and subject-specific information to capture not only the idiosyncratic information but also inform personalised assessment of individual longitudinal disease profiles. This may also potentially advance personalised medicine. We have data from immunological and haematological diseases; we also have data from wearable devices (digital health). The methods can be generalised to other disease territories.

Profil

• A master’s degree and an undergraduate degree in disciplines relevant to (applied) mathematics, computer science, engineering, machine learning, or statistics
• An interest in developing new methods and applications and employing them to address real-world healthcare-related problems
• An interest in data visualisation
• A team player
• The working language of the group is English. A good command of the french langage is mandatory
• Strong programming skills related to machine learning and longitudinal methods
• Experience in machine learning, statistical modelling, and version control.

Nous offrons

• 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 students to apply.
• Possibility to collaborate with international universities.
• Access to courses from the CHUV and the University of Lausanne.
• 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, 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 : Dre Nazanin Sédille nazanin.sedille@chuv.ch (https://www.chuv.ch/fr/laboratoires/dl-home/le-departement-en-bref/unites/service-de-chimie-clinique) and Professor Oliver Y. Chén olivery.chen@chuv.ch (www.oliverychen.com)

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.

Please send contact information for three references.

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 (Write an email / +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.

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