165 computational-physics "https:" "https:" "https:" "https:" "INRAE" positions at ETH Zurich
Sort by
Refine Your Search
-
scientometrics Profile Background Master’s degree ideally in information science, library science, data science, computer science, or a comparable field; a PhD is an advantage Regardless of academic background
-
COMPAS XR framework developed at ETH Zürich. Project background The successful candidate will work at the intersection of computational design, XR, human-computer interaction, and robotic fabrication, with
-
-dimensional biological datasets, developing and maintaining bioinformatics pipelines, and collaborating closely with experimental scientists to translate computational findings into biological insight
-
. Profile Applicants must hold a M.Sc. Diploma (120 ECTS points) or equivalent in civil, mechanical or electrical engineering, geosciences, physics, applied mathematics, computer sciences or related fields
-
of this position are (1) to manage the fabrication and characterization of ultrasound-sensitive drug carriers, (2) improving the manufacturing process and (3) establish the delivery of biologics such as RNA
-
evaluate prototypes together with industrial partners Profile Required experience CH/EU/EFTA citizenship or valid Swiss work permit PhD in Engineering, Computer Science, Robotics, or related field Strong
-
, aquatic biota Project background Many small riverine organisms rely on flow‑mediated processes to complete their life cycles. One such process is drift, a fundamental mechanism of downstream dispersal
-
. Profile Ph.D. in Nuclear Engineering, Physics, Chemical Engineering, or a related field. Experience with computational chemistry and simulation tools. Prior knowledge of nuclear safety, severe accident
-
of the student's PhD thesis. The PhD program at D-USYS (Department of Environmental Systems Science), ETH Zürich typically lasts three to four years on average. PhD students usually have to conduct research, study
-
100%, Zurich, fixed-term We have an open PhD position at the intersection of machine learning, embedded intelligence and human–computer interaction. The project will explore how learning systems can