10 deep-phd positions at Delft University of Technology (TU Delft)

PhD Position High-Fidelity Modelling of Intensified Hydrogen Production from Biomass

. Classical, experimentally based investigation methods, often fall short of providing sufficient details for a deep understanding because of the extreme conditions. Numerical simulations can prove very

PhD Position Machine Learning on Graphs

not only capture the graph structure of the data but also account for the dynamics of the topology as practical graphs change over time. In this PhD project, we are looking for a candidate to work on

PhD Position Understanding the Non-Stationary Nature of Human-Induced Earthquakes and its Impact on Geothermal Energy Production

Job related to staff position within a Research Infrastructure? No Offer Description The research programme DeepNL aims to improve the fundamental understanding of the dynamics of the deep subsurface

Assistant / Associate Professor Opto-Mechanical System Design

verge on the fundamental, e.g. by deep-diving into physical aspects affecting precision or accuracy. Alternatively, your research could be geared to developing new techniques and algorithms for novel

PhD Position Scientific Machine Learning for Extreme Fluid Dynamics

nonlinear interactions at the origin of such extreme events. In this project, we will explore the use of cutting-edge scientific machine learning framework that blends deep learning with physics-based

PhD position in Grounding Large Language Models in the Real World

, and deep learning is a plus Experience with affective computing and human-computer interaction is a plus Excellent written and verbal communication skills in English Doing a PhD at TU Delft requires

PhD Position Observability and Identifiability for Data Assimilation of Induced Seismicity in Groningen

Job related to staff position within a Research Infrastructure? No Offer Description The research programme DeepNL aims to improve the fundamental understanding of the dynamics of the deep subsurface

PhD Position Satellite-derived Bathymetry using Physics-informed Machine Learning

promising solutions. This project aims to enhance SDB accuracy through deep learning pan-sharpening and physics-informed machine learning techniques. These methods will be tested in two regions worldwide and

Postdoc Artificial Intelligence for the Dynamics of 2D Materials

include: Developing on "deep symbolic regression", i.e., an existing ML framework that uses neural networks to determine mathematical expressions from experimental or numerical data. Coordinate the work

Postdoc Artificial Intelligence for the Dynamics of 2D Materials

include: Developing on "deep symbolic regression", i.e., an existing ML framework that uses neural networks to determine mathematical expressions from experimental or numerical data. Coordinate the work