Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
analytics (statistical models, machine learning, uncertainty quantification) to monitor and predict cycling travel conditions from various perspectives (safety, crowding, travel time, comfort, etc
-
applications Strong analytical and problem-solving skills Good communication skills and ability to collaborate across disciplines Motivation to make a real-world impact in sustainable energy and industrial
-
mathematical modeling; fundamental understanding of fluid mechanics and soft matter physics; good quantitative skills and strong analytical capabilities; proven experience with experimental image and data
-
orders. Using 3D field recordings and environmental monitoring, you will uncover how different species behave in swarms, how they use sensory cues from their environment, bringing new hindsight about the
-
aims to rethink how soft robots can interact with their environment, focusing on large-area, multi-point contacts—similar to how an elephant wraps its trunk around an object. Unlike traditional robots
-
characterizing defects such as dislocations Applying generative models (e.g., GANs, diffusion models) to augment microscopy datasets Investigating domain adaptation techniques across different imaging modalities
-
and spoken English. - Strong analytical skills. - Willingness to acquire a variety of additional skills ranging from physics modelling and statistical data analysis to hardware programming. PhD
-
and enthusiastic researcher. Analytical skills, initiative and creativity are highly desired. You are a naturally curious person who is eager to learn more and has a strong interest in research
-
November 2025 or as soon as possible thereafter. This PhD project aims to explore how emerging datasets could provide value to the UK’s insurance industry through a combination of data analytics, modelling
-
data to decode multisensory information Investigate how neural representations change across different brain states (awake, asleep, engaged) and track representational drift over extended time periods