Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
, the projects combine classical microbiology, microbial physiology, experimental evolution, and omics-based approaches to link microbial interactions and system performance across spatial and functional scales
-
Job Description Are you interested in biomedical technology and how microdevices could improve diagnostics and patient monitoring? Would you like to contribute to the development of novel strategies
-
stipends. Stipend 1: AI-Driven Data Management for Knowledge Graph Construction and Querying You will develop methods for constructing and querying knowledge graphs, with a focus on integrating generative AI
-
working with deep learning software stacks, extensive software development experience, and knowledge of machine learning frameworks (such as transformers, torch, Megatron, triton etc.) are pluses. MSc
-
Job Description As part of our Novo Nordisk Foundation Challenge Grant Energy Materials for the GUT (EMGUT), we are seeking a highly motivated PhD student to advance the development and testing
-
Candidates (DCs). We aim to create a network of early-stage researchers equipped with knowledge and skills - including entrepreneurship – to develop and support implementation strategies that address
-
this knowledge to suggest new ways to optimise catalytic reactions. Overall, you will contribute to the development of AI-enhanced autonomous workflows for interface. These implementations will be general and will
-
challenges associated with capturing CO₂ directly from ambient air and is suited for candidates interested in combining computational modeling with experimental electrochemistry to develop energy-efficient e
-
, enthusiasm for technology and the drive to adopt new skills and extended responsibilities. We offer an open, international team with flexible work organization and support of individual development. The group
-
with the experimental group of Andras Kis at EPFL. Responsibilities The successful candidate will develop and apply ab initio computational methods rooted in many-body (perturbation) theory to explore