A Fully Funded PhD Student in Machine Learning and Health and Disease Management

Médico-technique

A Fully Funded PhD Student in Machine Learning and Health and Disease Management

We invite highly motivated candidates to apply for a fully funded PhD position to join Professor Oliver Y. Chén's team (www.oliverychen.com). The team works 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; and (d) federated learning. For this PhD position 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.

Professor Oliver Y. Chén's team develops new machine-learning and statistical methods and study large-scale data in health and disease. The 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.

The team's 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

With this full scholarship, the PhD student(s) will primarily work on three interlinked projects in collaboration between CHUV, UNIL, and Roche Diagnostics:

• Discovering correlates and eventually predictors relating to disease diagnosis, treatment, and management efficacy. Classical machine learning methods aim at predicting the disease status, unbiasedly estimating treatment effects, or minimising disease management costs. Such approaches, however, oftentimes optimise one objective (diagnostic accuracy, treatment effect, or disease management cost) while overlooking the others. Here, we will design a framework to identify, from multivariate, potentially high-dimensional predictors, those that optimise several objectives (e.g., high prediction accuracy, effective treatment, and affordable cost) based on real-world healthcare data.
• Longitudinal disease dynamics, treatment assessment and forecasting. Developing a new method that, retrospectively, (1) unveils the longitudinal trajectories of the disease profile, (2) forecasts future disease progression, and (3) compares the efficacy of the assigned disease treatment and management strategies.
• From expert-centred healthcare to a united triad knitting patients, experts, and healthcare systems. Leveraging the methods from (1) and insights from (2), we (re)define “best disease outcome measure” by embracing, balancing, and integrating (a) medical experts’ consideration (diagnostic accuracy and treatment effect), (b) patient-reported outcomes, and (c) healthcare systems’ sustainability (availability and cost of care).

The students will, if interested, collaborate with colleagues in other projects within and across teams.

The students have the freedom to propose and develop independent studies within the broader aims of this Scholarship and collaborate with or visit other teams.

The students will work in an interdisciplinary, multicultural environment.

The positions, once filled, may start immediately.

Profil

Minimum qualifications:

• 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 problems.
• An interest in data visualization.
• A team player.
• Proficiency in English.

Desired qualifications:

• Strong programming skills related to machine learning, and longitudinal, many-to-many methods.
• Experience in federated learning, machine learning, statistical modelling, and version control.

Nous offrons

• A fully funded PhD position that covers the tuition plus an annual salary (SNF salary scale).
• 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 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.

Contact et envoi de candidature

Please send Professor Oliver Y. Chén (olivery.chen@chuv.ch) the following.

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 have any technical question, please contact our Recruitment Team (021/ 314-85-70 from 08h30 to 12h00 and from 14h00 to 16h30).

Your file should include:

• A motivation letter (no more than one page)
• A CV.
• Copies of your undergraduate and master’s theses
• Contact information for three references

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 will not be accepted and cannot be charged. Thank you for your understanding.