A Fully Funded PhD Student in Machine Learning and Federated Learning
vor 3 Wochen
We invite highly motivated candidates to apply for a fully funded PhD position to join Professor Oliver Y. Chén's team ). 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.
Département DM - Dpt de médecine Code emploi Assistant Doctorant - 0131Niveau Hors DECFODate d'entrée souhaitée Type de contrat CDDDate de fin de contrat Catégorie professionnelle Médico-techniqueLieu LausanneTaux d'activité 80% -100% Date de début de publication Date de fin de postulation Référence 10691-MTContexte
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.
MissionUnder 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.
ProfilMinimum 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 federated learning
- Experience in federated learning, machine learning, statistical modelling, and version control.
- 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.
Please send Professor Oliver Y. Chén ) the following.
- A motivation letter (no more than one page).
- A CV.
- Copies of your undergraduate and master's theses.
- Contact information for three references.
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/ from 08h30 to 12h00 and from 14h00 to 16h30).
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.
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