PhD position in multi-agent modeling of integrated heating-electricity systems

The project of the PhD student based at CWI in Amsterdam will focus on techno-economic models (and in particular multi-agent modeling) of energy exchange and flexibility for positive energy districts. A specific focus will be placed on designing incentives and sharing models between prosumers in PEDs. This includes both peer-peer trading models between individual prosumers (based on their own energy assets such as PVs, home batteries, heat pumps etc.), and models where joint electricity and heating infrastructure is shared in a local cooperative or coalition.

Potential topics that are relevant for this PhD position include:

• Modeling of peer-peer energy exchanges between prosumers in energy communities, considering their energy preferences and constraints
• Coalitional models for efficient sharing of joint energy infrastructure (such as a community heating network or community battery). This can use, e.g. techniques from coalition theory such as Shapley values.
• Models of network congestion, and solutions for planning and co-optimizing electricity and heating networks to avoid congestion issues
• . Validation of proposed techniques through principled multi-agent simulations, complex systems analysis or other data-driven methods.

A PhD Student in Neuro-AI or Developmental Vision (M/F/X)

A PHD-studenT iN NeuroAI of Developmental vision (m/f/x)

to work on:

The Curriculum of Sight - How Visual Processing Develops in Brains and Machines.

For animals, it is imperative to learn as quickly as possible. To do this, we know that the mammalian visual system utilizes "inductive biases"—inherent assumptions that streamline learning for specific environments. In this project, we aim to investigate how rapid learning in mammals is facilitated by the learning curriculum shaped in the developmental stages of human infants. By comprehensively measuring and documenting these developmental stages and the associated inductive biases, we aim to identify key mechanisms that drive rapid learning in the visual system. The goal is to create a robust mechanistic neural network model of the visual system that not only mimics its processing capabilities but also its adaptability, leveraging early developmental data and ecological validity. Infant data will be acquired in collaboration with Dr. Tessa Dekker (University College London) and Dr. Ingmar Visser at UvA. The project thus aims to quantify and model the remarkable learning efficiency of the human visual system.

The project is an interdisciplinary collaboration between the the Machine Learning group at CWI in Amsterdam (Prof.dr Sander Bohte) and the Brain & Cognition group (Dr. Steven Scholte) at the UvA, where the PhD at CWI will focus on developing detailed and large-scale neural models of the early visual system.

PhD student (m/f/x) on the subject of Fast and low-dose medical imaging

The project is part of the IMAGINE open innovation lab (https://research.umcutrecht.nl/news/imagine-takes-off/), and aims to develop novel computational methods for faster medical imaging with reduced radiation dose. The focus will be on (cone beam) CT and its use in image-guided interventions in oncology. One of the main challenges is to deal with high noise levels (due to the low radiation dose). One possible way to address this is to incorporate information gained from previous scans (e.g., MRI) of the same patient.

As a PhD student on this project, you will:

• Design and conduct research on computational methods for CT reconstruction in the context of image-guided interventions, focusing on improving quality of cancer treatment for patients as well as reducing workload for clinicians. Collaborate with experts in AI, data science, and medical imaging to develop integrated solutions
• Engage in translational research, bridging the gap between technological innovation and clinical application
• Contribute to publications and presentations to disseminate findings within the scientific community
• Participate in interdisciplinary meetings and workshops to foster knowledge exchange and innovation

Your work is vital to advancing less invasive treatment options, reducing patient recovery times, and optimizing healthcare resources. Your day-to-day research will take place in the computational imaging group at CWI, where you will have a chance to interact and collaborate with fellow PhD-students and research staff with a background in mathematics, computer science, and imaging science. It is expected that you will frequently interact with the other members of the IMAGINE open innovation lab (situated in Utrecht), as well as contribute to teaching at the Mathematical Institute at Utrecht University.