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who meet the following criteria: PhD in a related discipline. Expertise in one of the following areas: Single-cell and spatial transcriptomics, whole genome sequencing, proteomics, organoid modelling
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-up. In the ZonMW-funded AI for EVAR project, we develop multi-modal models for optimized selection of treatment before, and follow-up after EVAR. You will implement and advance multimodal deep learning
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In this Postdoc you will investigate methods for risk-aware navigation among decision-making agents, using learning and optimization, and apply them to self-driving cars and teams for quadrotors
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Discrete Element Method (DEM) models to simulate the charging and formation of multi-component, poly-disperse burden mixtures in the blast furnace. The goal is to optimize burden structure and permeability
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wide range of resources and is mostly not publicly available. While sharing proprietary data to train machine learning models is not an option, training models on multiple distributed data sources
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wide range of resources and is mostly not publicly available. While sharing proprietary data to train machine learning models is not an option, training models on multiple distributed data sources
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of HBGI interventions at three European sites; · Monitoring of physical, ecological, and social performance compared to traditional grey infrastructure; · Iterative design optimization in
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, rental platforms, and production systems—where decision-making must balance conflicting objectives, leverage real-time data, and ultimately support sustainable profitability. Examples include optimizing
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with future developments in the industrial ecology field What you bring This position is a good fit for you if you recognise yourself in the following: You hold a PhD in one these fields
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-derived organoid models. You will work closely with in-house technology platforms, including the Single Cell Genomics Facility, Big Data Core and High Throughput Screening Facility. Our research is embedded