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short-term physiological responses of tree species and modified long-term dynamics of the whole ecosystem. On the other hand, vegetation demography models are numerical tools formulating forest processes
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, biochemists and technicians. The multidisciplinary research in our group covers a wide range of topics from radiobiology, radiation physics and space research to radiation therapy. The radiobiological modelling
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Programming Beyond the Circuit Model” led by Assoc. Prof. Robin Kaarsgaard . The project aims at introducing new abstractions, models, and programming languages that radically change how we can reason about and
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, integrative biology approach that utilizes human pluripotent stem cell based model systems, high throughput functional genomic screening and big data based machine learning, bridging the scales from genetics
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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
produced by PBF-LB. After identification of the most relevant parameters adopting a design of experiments strategy, a probabilistic (e.g. Gaussian Process Regression) model to describe the relationship
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quantitative image analysis, numerical modeling, and explainable AI (XAI) with state-of-the-art biophysical methods. Using techniques such as traction force microscopy, microfluidics, 3D bioprinting, and
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systems and in vivo models in combination with cell state and mutation reporters and single-cell technologies with spatial readouts to study the impact of intestinal microbes and their metabolites on colon
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, making exciting new strides into uncharted territories, we investigate how cell differentiation, pre-existing immune conditions, superinfections, aging, etc. affect the infection outcome using model
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resolution across large genomic datasets. We focus on cancer models (osteosarcoma, breast cancer, leukemia) and on neural progenitor cells to understand how genome instability contributes to tumor initiation
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics