14 machine-learning-phd Postdoctoral positions at Delft University of Technology

Postdoc Data-driven Prediction of Human Body Motion in Automated Vehicles

-driven control algorithms, biomechanical modelling, system identification, machine learning, control theory. Prior experimental experience on human body dynamics and motion comfort. A strong academit track

Postdoc Data-driven Ultrasonic Welding of Thermoplastic Composites

related field. Proficiency with machine learning methods and corresponding software packages is a plus. Experience with ultrasonic welding is a plus. Fluency in English and proven academic writing skills

Researcher on human-in-the-loop system design for automated driving

participants, machine learning. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities (based on scale 10: €3.226,00 - €5.090,00). Depending on your knowledge and

Postdoc Silico Modeling and Optimization of Soft Robotic Hearts

a PhD in aerospace engineering, applied mathematics, mechanical engineering or other related fields. Affinity with physics-informed machine learning, computational VVUQ (verification, validation, and

Postdoc Scaling Interconnects for Ultra High-Throughout Chip Assembly

. Challenges include the identification of suitable metallurgy, the reproducible fabrication of the interconnects, and modeling of their properties. The results of the project are expected to lead a new machine

Postdoc Reinforcement Learning and Control: An Algorithmic Approach

been well understood to this date, primarily due to the missing link between data analytics techniques in machine learning and the underlying physics of dynamical systems. The goal of this project is to

Postdoc Multi-robot Mobile Manipulation in Human-Centered Environments

/ The candidate has (or will soon have) a PhD degree in Robotics, Controls, Machine Learning, or a related field. The candidate must be able to work at the intersection of several research domains, have a proven

Postdoc in Machine Learning-driven Modeling of Metallic Microstructures

Challenge: Solve computational bottlenecks in the modelling of mechanics of metallic systems. Change: Develop new physics-informed machine learning algorithms and predictive models. Impact: Enable

Postdoc Composite Design and Manufacturing Using Applied Machine Learning Methods

Explore and develop scalable machine learning methods for aerospace structures' design and manufacturing This postdoctoral research aims at exploring statistical methods that enable the data-driven

Postdoc Design of Multi-Modal Pixelated Capacitive Sensor Chip for E-nose

. The data acquired with your chip(s) will be analyzed with machine-learning algorithms. You will collaborate with researchers and companies of various disciplines like chemistry, embedded systems, software