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will evaluate potential biases among different measurement approaches —including thermochronology, cosmogenic nuclides, and suspended sediment records— and apply advanced statistical analyses and erosion
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approaches treat NP design as static property prediction. This project takes a fundamentally different approach: using generative models to propose novel NP formulations and coupling them with explainability
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optical probes on micro-photonic chips for submicron-scale optogenetics, the development of a neuron–computer interface system, and the use of iPSC-derived human neurons for all-optical physiology
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for collaboration with experimental groups working on physical learning in electronics, mechanics, and living flow networks (Physarum Polycephalum). For more information about our work, see: [1] Stern, Hexner, Rocks
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related to endocrinology and/or genetics (preferred); the social skills to motivate people from different backgrounds and facilitate team-work; substantial and demonstrable experience with academic writing
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from the areas of few-shot learning, continual learning and modular deep learning, as well as different LLM alignment frameworks, based on reinforcement learning and direct preference optimisation
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and verification techniques for the design of the Drag-Free Attitude and Orbit Control System of the Next Generation Gravity Mission. To achieve this goal, four different objectives must be fulfilled
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chemistry, or a closely related subject is required for this project. You have to enjoy working with optics and ultrafast lasers and have some affinity with electronics. If you have experience with one
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carrying different combinations of bacteria and phages, using mRNA, small RNA and single-cell RNA sequencing. In addition, you may also contribute to ongoing bioinformatic projects in the lab. Tasks and
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of various hydrogen generation technologies: water electrolysis (different types, alkaline, PEM, SOEC, AEM), methane pyrolysis (plasma) and system integration. Our focus in on the system integration, taking