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of novel physics-guided AI algorithms for drug design, integrating physics-based modeling with state-of-the-art deep learning methods. The project will focus on creating a next-generation docking framework
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Your position The candidate will have the opportunity to exploit some of the cutting-edge experimental and computational methods, comprising constraint-based and kinetic modeling, statistical
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have since helped halve global mortality, but this progress is threatened by rising insecticide resistance. We build quantitative, data-driven models to forecast the spread and impact of resistance
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for FGT 3.0: confidential HPC pipelines, scalable training infrastructure, and fine-tuned medical LLMs trained on clinical guidelines, evidence-based datasets, and real-world medical corpora. These models
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modification of primary human immune cells (T cells and macrophages). Conduct in vitro validations using advanced models, including patient-derived organoids and co-culture systems. Perform in vivo validations
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electron microscopy, atomic force microscopy, metal evaporation, wire bonding), performing computer-based simulations and modelling in COMSOL, physics of strongly correlated many-body systems. We offer you
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processing (reactive ion etching, optical/electron beam lithography, scanning electron microscopy, atomic force microscopy, metal evaporation, wire bonding), performing computer-based simulations and modelling
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of adaptive radiation and associated key innovations in the evolution of freshwater diatoms. By integrating morphology, physiology, genomics, transcriptomics, and computational modeling, we aim to (i) determine
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reinforcement learning for large language models (LLMs). Research directions include developing next-generation post-training algorithms, exploring diffusion-based approaches to reasoning with language models
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scanning probe instrumentation fabrication of van der Waals heterostructures transport, optical spectroscopy, and quantum sensing experiments data analysis, modeling, and scientific communication