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

vor 3 Wochen

Lausanne, Schweiz Centre Hospitalier Universitaire Vaudois Vollzeit

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, unveils the longitudinal trajectories of the disease profile, forecasts future disease progression, and 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 and insights from , 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.

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