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dynamics, Numerical Modeling, Scientific Computing, Applied Mathematics, Computational Chemistry). Experience in reactive transport modelling and strong background in reactive transport processes. Experience
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will be tailored to your expertise, spanning from hardware design to system-level optimization and control methods. For the AI position, you will develop machine learning models that incorporate physical
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molecular biological and quantitative techniques including microscopy, flow cytometry, computational/mathematical modeling, and utilizes approaches from synthetic biology and complex biological systems
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skills, proficiency in quantitative analysis of large datasets and working with pre-trained machine learning models is desirable but not essential To apply online for this vacancy and to view further
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pathways driving leukemic transformation during haematopoietic development. The successful candidate will use in vitro differentiation systems (ESC/iPSC) to model normal and malignant haematopoiesis
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ecological systems with frequency-dependent selection. Planned projects use dynamical systems, stochastic differential equations and agent-based models, statistical methods for parameter inference, network and
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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, modeling of topological superconductors, protocols for observation of nonlocal quantum information storage, topological effect in open system, or next generation superconducting qubits using hybrid materials
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genomics, virtual cell models Graph-based neural networks, optimal transport Biomedical imaging, deep learning, virtual reality, AI-driven image analysis Agentic systems, large language models Generative AI
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-doctoral researcher with a strong quantitative background in biology, evolution, or social science, and previous experience in mathematical modeling or behavioral experimentation. Purpose: Complete a multi