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-life conditions; You will perform structure-property correlations and unravel the physical and chemical characteristics that determine the functional properties of the materials; You will collaborate
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, enabling enhanced reasoning, adaptability, and performance in real-world scenarios. The position is embedded within the Information Retrieval Lab (IRLab), under the supervision of Maarten de Rijke. It is
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large genomics data and later incorporating the testing on known unwanted linkages. You will primarily be responsible for building a machine learning model and performing computational genomics
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. You will primarily be responsible for building a machine learning model and performing computational genomics or quantitative genetics on new sequencing data. Your goal will be to develop a system that
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generative model. As such, GenIR models are able to more deeply interact with the underlying corpus, enabling enhanced reasoning, adaptability, and performance in real-world scenarios. The position is embedded
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contract, we initially offer a one-year contract that can be extended for 2 more years on good performance. Of course, we are happy to have a conversation with you if you have any questions about this
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technologies—and eager to accelerate their discovery with machine learning and materials theory? Are you passionate about linking atomistic processes to device performance through computer simulations? Are you
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researcher will perform the following tasks: 1) Collecting and reviewing relevant academic literature concerning the research theme; 2) Designing and conducting experiments with students as
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Challenge: Managing flood risk in vulnerable interconnected
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Challenge: Managing flood risk in vulnerable interconnected regions. Change: Advancing hydraulic modelling and NBS for resilience. Impact: Protecting communities with smarter adaptive strategies