A Fully Funded Phd Student in Machine Learning and

vor 5 Monaten

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**:
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.

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