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. We will prioritize hits with suitable predicted drug metabolism and pharmacokinetic properties for optimization using organic synthesis. Finally, you will validate specificity using biophysical methods
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improving prediction accuracy by at least 25%, and joint management of radio, optical, and compute resources in multi-tenant environments to increase utilization by up to 20% through smart slicing. In
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enablers for predicting and reasoning about dynamics and transport patterns, in turn being of key importance for a broad range of human activities. The research in the PhD project will focus on core spatio
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and predict the sensitivity and resilience of Swedish conifer forests to interacting soil fertility and droughts. Specifically, we want to detect whether long-term fertilization practices have modified
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
energy systems. The global focus on safety, predictive maintenance, and lifecycle cost reduction. The rising availability of high-fidelity NDT data and accessible machine learning tools. In summary
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will design “smart” aqueous or gel electrolytes and matched cathode coatings that work as a single system to: control how Zn2+ is solvated and transported, suppress dendrites and cathode dissolution, and
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potentials to interpret experimental data and predict catalytic performance. The tasks can include: Advancing equivariant neural network potentials (ENNPs) to model nanoparticle energy surfaces. Building atom
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particle clustering and morphology affect strain localization and damage evolution. Integrate experiments and modelling to create predictive tools for recycled alloy performance. Your immediate leader is
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: Identification of EEG and fMRI biomarkers with potential predictive value for psychedelics’ antidepressant effects (National Institute of Mental Health (NIMH), Czechia) DC4: Mapping the subjective phenomenology
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potentials to interpret experimental data and predict catalytic performance. The tasks can include: Advancing equivariant neural network potentials (ENNPs) to model nanoparticle energy surfaces. Building atom