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models and reinforcement learning models for 3D graphs of materials to explore vast inorganic chemical spaces and design synthesizable energy materials. You will couple such models with physics simulation
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federated knowledge graph framework that facilitates the querying, consolidation, analysis, and interpretation of distributed proteomics-focused clinical knowledge graphs. To achieve this, we will employ
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Job Description Are you passionate about chemical theory and computation? Are you interested in the real-time (atto- to picosecond) dynamic changes in molecules when they undergo physical and
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Compute we are seeking two outstanding postdoctoral candidates to work on theory of novel protocols, stronger security proofs, and efficient numerical techniques for semi-device-independent quantum
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tools will be a strong advantage but is not a strict requirement. We expect you to bring an interest in control theory, building energy systems and modelling, and to enjoy working with both theory and
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. The ambition is to develop lab exercises that directly support and illustrate the abstract mathematical theory of the subjects, and to measure whether such exercises improve the learning process. You will be
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create multi-fidelity predictive models that integrate data from quantum simulations and experiments, using techniques such as equivariant graph neural networks with tensor embeddings. We aim to train
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help bridge the gap between theory and application, enabling the design of next-generation devices. Responsibilities and qualifications Your main purpose will be to develop computational models
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Job Description We are seeking an outstanding postdoctoral candidate in the field of theoretical catalysis. The postdoc will be a member of the Catalysis Theory Center (CatTheory) at the Technical
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complex droplet flow patterns and the relations with the chemistry. One successful theory for optimization of surfactant impact and blends is the hydrophilic-lipophilic deviation balance. This enables us to