Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
-
Field
-
health. We are looking for a proactive and skilled Software Engineer to join the newly forming research group Analytics and Informatics for Child Health (AICH) at the Department of Biomedical Engineering
-
afterwards. You will work closely with our research team to implement a new version of our RAG-based chatbot. Profile The ideal candidate will be a computer or data science student, or a student with extensive
-
multidisciplinary research program and collaboration network in the area of Culturomics. Promising candidates possess an interdisciplinary profile that combines outstanding microbiological expertise with excellent
-
mentoring programme. Support the development and implementation of training and capacity building of relevant stakeholder groups. Engage with various stakeholders from the private and public sectors as
-
developer with a strong background in scientific computing who is eager to lead a development team, drive innovation, and explore commercial opportunities arising from our research. The position is linked
-
Your position Our group conducts research at the intersection of artificial intelligence (AI) and pediatric healthcare, developing AI and machine learning (ML) methods to address real-world clinical challenges. Our core research topics include but not limited to the following...
-
Required experience: CH/EU/EFTA citizenship or a valid work permit for Switzerland A Master’s degree (ETH, university) in engineering, computer science, or a related field Strong academic performance meeting
-
Your profile Candidates should have an exceptional academic record and a robust mathematical foundation. They should have published works at the main conferences in the field of machine learning, such as ICML, NeurIPS, ICLR, etc. Excellent communication skills and fluency in English (spoken and...
-
Emulators of Stochastic computational models"), funded by the Swiss National Science Foundation (SNSF). The project aims to significantly advance the state-of-the-art in uncertainty quantification (UQ) by
-
, electrical engineering and computer science to design highly efficient and sensitive imaging and inference approaches to help guide diagnosis and treatment in cardiovascular patients. Project background Our