Sort by
Refine Your Search
-
. The tasks will require a high degree of innovativeness, command of multi-scale asphalt and binder testing methods, as well as knowledge of the pavement design concepts. Experience in material modeling and
-
. Empa is a research institution of the ETH Domain. Empa's Laboratory 'Particles-Biology Interactions' and its group 'Multi-omics for healthcare materials' are looking for a candidate for an
-
energy planning to national system studies of Switzerland, and European-wide multi-sector energy integration. Job description Main Tasks Apply our in-house energy system modelling frameworks to analyze and
-
first insight and publish a first paper at an early project stage. In summer 2026 you will then lead a controlled infestation experiment in our greenhouses in combination with some field work. You hold a
-
of our Materials Vision Tech initiative, we focus on multi-element gradient thin film systems, i.e. their rapid deposition and automated multi-technique characterization. Within the Swiss-Polish innovation
-
to enhance the efficiency and performance of ferroic materials. We are particularly excited to explore ferroic phase transitions and the emergence of complex configurations that cannot be captured by
-
Thun explores the possibility of high throughput materials development. In the context of our Materials Vision Tech initiative, we focus on multi-element gradient thin film systems, i.e. their rapid
-
interdisciplinary expertise in energy transitions, with a solid understanding of photovoltaics, renewable energy integration, multi-energy systems, and energy conversion and storage technologies. You have strong
-
to work with a diverse, motivated, and multi-cultural team in a creative research environment. Support for personalized professional development and mentoring with the ability to build a strong support
-
Metagenomics, meta-transcriptomics and metabolomics data analysis and familiarity with gut microbiome research. Machine learning for genomics (representation learning, generative models, causal inference). Multi