Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
-
Field
-
later than February 2026. Profile PhD degree in biochemistry, molecular biology, structural biology or related curriculum. Motivation to tackle complex and interesting mechanistic questions. Research
-
through the EU Research Framework Programme? Horizon Europe - MSCA Is the Job related to staff position within a Research Infrastructure? No Offer Description EU Marie Skłodowska-Curie Actions (MSCA) PhD
-
contribute to diverse machine learning projects across ETH's research and administrative domains, developing and implementing scientific computing solutions to support various projects. Throughout all your
-
operates in English, strong English skills are essential. Further project details will be discussed during the interview. The anticipated start date is no later than February 2026. Profile PhD degree in
-
? No Offer Description PhD position in Computational mechanics The Computational Mechanics Group in the Department of Mechanical and Process Engineering of ETH Zurich is seeking one doctoral student
-
Mixed Reality. This research combines physiological time series analysis (specifically EMG during muscle activation), machine learning, and real-time system design for intelligent interaction systems
-
PhD candidate to investigate how DNA is translocated from the extracellular environment into the bacterial cytoplasm, where it is incorporated into the genome. To do this you will: Express and purify
-
at the Department of Biosystems Science and Engineering (D-BSSE) of the ETH Zurich in Basel invites exceptional candidates to apply for PhD and postdoctoral positions in pioneering projects developing synthetic
-
microfluidic and mechanical loading platforms Support experimental validation, analysis, and documentation for in vitro models Profile PhD in biomedical engineering, bioengineering, microtechnologies, or related
-
Metagenomics, meta-transcriptomics and metabolomics data analysis and familiarity with gut microbiome research. Machine learning for genomics (representation learning, generative models, causal inference). Multi