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A Fully Funded Phd Student in Machine Learning and

vor 3 Monaten

Lausanne, Waadt, 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:

Under this fully funded PhD position, the student will primarily work on three projects:


  • Building better biomarkers for predicting disease onset and severity via federated learning (FL). Inventing new machine learning methods, via a FL network of Electronic Health Record data, to identify clinical variables for early identification of patients with cardiometabolic, infectious, immunological, neurological, and oncological diseases and to predict disease severity
-
Integrating sites into a federated learning network. Working as part of a team to establish a new FL network using existing relationships with healthcare providers to ensure best practices for data processing and curation and to equip the sites with new methods and algorithms
-
Generalized federated learning (GFL). Leveraging insights from Project 1 and the infrastructure built via Project 2 to establish a technical and methodological framework for developing and validating new algorithms.


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

The students will work in an
interdisciplinary,
multicultural environment.

The position, 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
    data visualization:
  • A
    team player:
  • Proficiency in
    English.

Desired qualifications:

  • Strong programming skills related to machine learning and federated learning
  • 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.
  • A motivation letter (no more than one page).
  • A CV.
  • Copies of your undergraduate and master's theses.
  • Contact information for three references.