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  • Writer's pictureOmran Aburayya

Fully funded PhD positions in Machine Learning and AI at Delft University

Updated: Jun 13

Machine Learning

The Faculty of Electrical Engineering, Mathematics & Computer Science at Delft University has multiple vacant salaried PhD positions in the fields of Engineering, machine learning, and artificial intelligence.

These PhD positions are salaried at € 2770 per month in the first year to € 3539 in the fourth year.

Below we list each position with a brief description and a link for more details and online application.

 

1). PhD position: Big Data Analytics and AI in Port Performance Optimization – Infrastructure Use

As a PhD candidate in Infrastructure Use, you will play an important role in exploring the operational dimensions of port infrastructure through the lens of data availability and advanced AI models. The core objective of this position is to uncover added value for port applications at the operational level. Your focus will be on addressing diverse challenges, with a central emphasis on Port Nautical Safety. This involves the analysis of moving vessels, requiring the use of AIS and other sources of data, and translating this information into insightful assessments of trips and loads.


 

2). PhD Position Big Data Analytics and AI in Port Performance Optimization – Infrastructure State

As a PhD candidate in Infrastructure State, you will play an important role in exploring the strategic dimensions of port infrastructure through the lens of data availability and advanced AI models. The core objective of this position is to identify added value for port applications at the strategic level. You will tackle a range of complex problems, with a primary focus on port accessibility and dredging strategies. This entails a nuanced examination of how maintaining different bed levels can impact port performance, optimize dredging costs, integrate with tidal windows, and adhere to priority rules to achieve a desired service level.

The scope of your PhD extends to critical areas such as asset management, infrastructure monitoring, predictive maintenance, infrastructure resilience, space requirements, and infrastructure investments.


 

3). PhD position Secure Data Sharing in Federated Edge Clouds

As a PhD student, you will design novel algorithms for real time exchange/sharing of data, inspired by challenging data sharing problems in automated driving, smart cities and smart grids

This PhD position will be based in the Network Architecture and Services (NAS) group in the Department of Quantum& Computer Engineering, co-supervised by Maksim Kitsak, Lydia Chen (TU Delft and University of Neuchatel) and Piet Van Mieghem. In your role, you will work closely with other PhD students of the NAS group and KPN staff.


 

4). PhD position Safe Use of AI in Telecommunication Networks


Research challenges include:

  • How to model the interaction between the different software components (NFs, automated network management), including the interplay with the underlying (virtualised) physical infrastructure

  • How to model the impact of AI (closed loop learning) on the potential “action spaces” of the different software components and the resulting potential network behaviour

  • Develop concepts of observability and controllability for AI based NFs and automating telecom network management.

  • Develop design guidelines for AI based NFs and automating telecom network management to guarantee network safety and security and to be able to explain (backtrack) in case of failure (explainable AI).


 

5). PhD Position Multi-Sensor Multi-modal Machine Learning for Social & Affective Computing

This project is part of a larger ERC funded project (NEON) that develops a novel framework and learning approaches to learn narratives of intention from multiple perspectives in in-the-wild ecologically valid social situations such as social networking events.

The successful applicants will develop automated techniques to analyse multi-sensor data (video, acceleration, audio, etc) of human social behavior.

Successful candidates will work in a team of 6 on the development of artificial systems that each integrate different aspects of machine learning, multimodal sensing, ubiquitous computing and social science


 

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