173 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL" positions at ETH Zurich
Sort by
Refine Your Search
-
incorporating machine learning. 2. Transcriptome Recording and Cellular History Reconstruction We are advancing our CRISPR-based transcriptional recording method (Schmidt, Nature, 2018; Tanna, Nature Protocols
-
methods. Contributing to the development, adaptation, and application of machine‑learning models tailored to RODI data (in collaboration with project partners). Designing and implementing an innovative
-
upcoming areas off the beaten paths. Our three main areas of research are machine learning, distributed systems, and theory of networks. Within these three areas, we are currently working on several projects
-
) or equivalent in civil, mechanical or electrical engineering, geosciences, physics, applied mathematics, computer sciences or related fields, and be at the beginning of their research career. Principal
-
contributes to positive change in society You can expect numerous benefits , such as public transport season tickets and car sharing, a wide range of sports offered by the ASVZ , childcare and attractive
-
with the Sinergia project partners You can expect numerous benefits , such as public transport season tickets and car sharing, a wide range of sports offered by the ASVZ , childcare and attractive
-
applied project frameworks in urban transformation. You can expect numerous benefits , such as public transport season tickets and car sharing, a wide range of sports offered by the ASVZ , childcare and
-
, which not only supports your professional development, but also actively contributes to positive change in society You can expect numerous benefits , such as public transport season tickets and car
-
discovery, and machine learning. In the wake of quantum mechanics' initial breakthroughs, we're on the brink of a second quantum revolution. Quantum physicists are adopting machine learning to explore complex
-
space applications. We combine theory, physics-based simulations, machine learning, and autonomous workflows to understand and design materials that can perform under conditions where conventional