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position is supported by a wider project, which is monitoring ecosystem functioning in tropical montane cloud forests (TMCF). The core study site is in a Peruvian TMCF, where several large-scale experiments
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require delicate tuning of multiple parameters, which often need to be performed manually. For each specific HCF design, an optimized fabrication process can take many iterations. Therefore
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great advantage: Forest and wood production processes Wood construction Furniture manufacturing Wood material science Machine learning Process simulation and optimisation The postdoctoral fellow is part
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also pursue multivariate time series analyses of monitoring of coastal fish communities in the Baltic Sea. The post-doc will lead and contribute to related scientific publications, and contribute
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and future developmental timepoints from time-series data. Our recent theoretical work suggests that these learned relationships can generalize across conditions, and we will test this using both
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The postdoc fellow will conduct research in the intersection of AI/Machine Learning and Software Technology. The advertised position will be placed in the DISTA research group (https://lnu.se/en/dista
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computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration
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workplace, conditions and benefits. Work tasks The research for this position will be within the research profile GRACE which focus on circular and sustainable product and production development, funded by
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Injection Systems (CIS) — natural protein machines used by bacteria to deliver molecular cargo. The group's mission is to understand the structure, function, and application of CIS for use in both
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, machine learning, and bioinformatics tools. Expertise in CRISPR-based assays, especially CRISPR screening, is highly meriting, as is experience with single-cell RNA sequencing or other omics assays