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of the life sciences at the Technical University of Braunschweig with an evolutionary biology perspective. In doing so, it should expand the existing focus areas of systems biology, biotechnology, biochemistry
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learning, new algorithms, and AI for Science. The graduate program at CMLR is an interdisciplinary, international program aimed at producing the best talents at the frontier of all disciplines where machine
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the Division of Systematic and Evolutionary Botany. Research in our group focuses on how genome-level processes (e.g. gene duplication, horizontal gene transfer, introgression) and natural selection have shaped
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and Evolutionary Ecology in the working group of Christa Schleper (Archaea Ecology and Evolution). Our research focuses on the physiology, ecological distribution and evolution of archaea, with a focus
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service. Job Responsibilities: Independently perform basic and advanced level statistical analysis, algorithm implementation, programming from a variety of biotechnology platforms, and oversee quality check
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network data science algorithms for mining molecular multi-omics and medical data to improve multiple tasks of precision medicine and discover new precision therapeutics. The successful candidates will work
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evolutionary history, or integrative analyses of genomic and clinical data. Analyses of non-cancer-related sequencing data may also be considered. The work will be carried out in a research environment with
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: developing systems where algorithmic decisions can be traced and audited; ensuring that outcomes are verifiable against standards and rules; (ii) transparency and explainability: creating interpretable models
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us. II. Positions and Requirements Position 1: Scientist in Intelligent Biobreeding Algorithm Responsibilities: Lead the formation and direction of an interdisciplinary team to integrate AI
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DC-26094– POSTDOC/DATA SCIENTIST – AI-DRIVEN CLIMATE RISK MODELLING AND EARLY WARNING SYSTEMS FOR...
applicant will contribute to the AIGLE project by: · Developing innovative scientific Deep Learning/Machine Learning algorithms for flash flood forecasting. · Contributing to the collection