Research Assistant Data Analysis - Bern, Schweiz - Universität Bern

Beschreibung

Department of Diabetes
Endocrinology, Nutritional Medicine and Metabolism and the Diabetes Center Berne
Start of employment:
Employment is fixed term for 6 months.

The Sensing and Monitoring Lab () is embedded into the interdisciplinary environment of the University of Bern, Inselspital Bern, and the Diabetes Center Berne. By combining expertise from the fields of engineering, medicine, and computer science, the samlab focuses on developing innovative sensing technologies that can enhance the management of diabetes.

Tasks The successful candidate will be involved in developing algorithms for our Moonwalk project. The candidate will:

Support the team in data analysis of radar sensor data for tracking animal health

Apply signal processing methods to time series data

Develop and evaluate predictive models (e.g., random forests, neural networks)

Implement methods for the explainability of the predictive models

Clean and visualize data

Install sensors in animal facilities

Requirements We are looking for enthusiastic candidates with excellent technical skills to join our research group. The following qualifications are required:

Currently enrolled in Bachelor or Master studies at the University of Bern

Student in Computer Science, Math, Biomedical Engineering, or in a related field

Ability to be on-site in Bern

Strong expertise in the development of algorithms

Knowledge of Python including relevant data science, neural network, and deep learning libraries

Excellent problem-solving, critical thinking and communication skills

Ability to work independently and as part of a team

Excellent English language skills

We offer

We provide excellent scientific expertise in a young and dynamic environment, and we offer support for a global scientific career with a focus on translational research.

You will be hired at the University of Bern as a part-time (approx %) research assistant (Hilfsassistent:in).

Application and contact For subject-specific questions and information about the position, please contact Prof. Lilian Witthauer, Interested candidates should submit their full application including a detailed CV, a cover letter describing training and research experience and transcripts of degrees via the Apply button.