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.