Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Delft University of Technology (TU Delft)
- Eindhoven University of Technology (TU/e)
- NTNU - Norwegian University of Science and Technology
- Carnegie Mellon University
- NTNU Norwegian University of Science and Technology
- Queensland University of Technology
- Tilburg University
- Chalmers University of Technology
- Luxembourg Institute of Science and Technology
- Monash University
- THE UNIVERSITY OF HONG KONG
- Technical University of Munich
- University of Southern California
- 3 more »
- « less
-
Field
-
Are you passionate about human movement and developing clinical tools for understanding human movement disorders? Job description Challenge: Over a quarter of a million people in the Netherlands alone have rheumatoid arthritis (RA) with a majority suffering from debilitating pain and...
-
Research Framework Programme? Horizon Europe Is the Job related to staff position within a Research Infrastructure? No Offer Description Build the next generation of railway track health assessment. Job
-
Build the next generation of railway track health assessment. Job description Railway bridges and the tracks they support are critical components of the railway network, directly affecting safety
-
causal inference, integration of heterogeneous data sources, uncertainty quantification Work with a wide range of data types, for example dietary records, biomarkers, omics data, registry data, and sensor
-
criteria Machine Learning Expertise: A robust foundation in probabilistic modeling, Bayesian inference, deep learning, and/or anomaly detection Modeling & Simulation Experience: Familiarity with Building
-
beam (FIB) imaging - can be combined with AI to reconstruct nanoscale chip structures and infer functional behaviour from physical layouts. The project addresses the challenge of extracting reliable
-
(PEM), and focused ion beam (FIB) imaging - can be combined with AI to reconstruct nanoscale chip structures and infer functional behaviour from physical layouts. The project addresses the challenge
-
investigation, one may aim to infer hidden geological or physical structures from measurements such as seismic, electromagnetic, or other indirect observations. Similar challenges also arise in climate and
-
, programmable OpenFlow/P4 switches and AI‑Boxes for fast inference, together with NetFPGA/DAG hardware for sub‑millisecond failure detection and a Timeseries‑DB/Grafana monitoring platform for closed‑loop testing
-
inference, counterfactual explanations, or uncertainty quantification in deep learning Evidence of high quality scientific writing, publications, a strong master's thesis, research software, or relevant open