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宁波诺丁汉大学招收2024年春季入学全奖博士生（方向：生物膜科学与工程）
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宁波诺丁汉大学招收生物膜科学与工程方向全奖博士生（2024年2月入学） 课题名称: 以混合物种微生物生物膜表征的电化学数据为对象的概率机器学习分析 probabilistic machine learning analysis of electrochemical data for characterization of mixed-species microbial biofilms 课题介绍： biofilms are microstructured microbial communities that thrive at surfaces. biofilm “mode of life” entails a broad range of interactions among cells and between cells and the environment. the combined effect of these dynamic interactions is the aggregation of cells at the interface and the production of extracellular polymeric substance (eps) that keep the cells close to each other and attached to surfaces. biofilms are open systems, so they are naturally occurring as mixed-species communities, comprising bacteria and fungi. mixed-species biofilms are a concern in healthcare, as they result in difficult-to-treat infections and they harbour antimicrobial resistant microorganisms. while natural biofilms comprise of multiple bacterial and fungal species, most studies still concern single species biofilms, which present an unrealistic response to antimicrobial agents, thus requiring costly animal model experiments for further validation. currently, there is still a knowledge gap in the understanding of mixed-species biofilm in open systems and there is not yet a single technology to monitor mixed-species biofilms. the availability of low-cost methods to characterize mixed-species biofilms will allow the rapid identification of pathogens in mixed-species biofilm infections and in water systems. overall, there is an urgent need for rapid and low-cost methods for mixed-species biofilm characterization in biomedical, environmental and bioprocess industry. biofilm electrochemistry can contribute to the resolution of mixed-species biofilms, due to its low cost, real-time and non-destructive characteristic. while biofilm electrochemistry cannot provide a final identification of each microbial species, it is in theory possible to analyse the specific signature of each microbial species using probabilistic machine-learning (pml) methods. this project aims to develop a novel method for real-time, online characterization of mixed-species biofilms using bioelectrochemical methods in combination with pml driven data analysis. the phd student will focus on the writing of the probabilistic machine learning (pml) code and electrochemical data analysis (acquired by another phd student in our group) for the modelisation of mixed-specie biofilms. s/he would have a background in data science/computer science/physics/applied mathematics/engineering with a strong interest in mathematical and computational biology. the phd student will work closely with the other phd student in the project to optimize the data acquisition pipeline. s/he will be co-supervised by prof alberto d’onofrio, a pml and mathematical/computational biology expert at university of trieste, italy and by prof daniele garrisi, an expert mathematician from unnc. the project might include a short-term secondment at university of trieste for training in mathematical methods and data analysis. 申请要求： 本科和研究生阶段成绩优异，一般要求：英式本科不低于二等上，且硕士不低于65分；美式本科GPA不低于3.0/4.0，且硕士GPA不低于3.5/4.0；中式加权均分本科不低于80分，且硕士不低于85分。 如果是本科申请直博，则要求本科学校是985、211或双一流，加权均分不低于85分。 英语语言成绩要求： 1、雅思：均分在6.5分及以上，各小项分数不低于6.0分； 2、托福：均分在87分及以上，speaking不低于20分，其余小项不低于19分； 3、pte academic：均分在71分及以上，小项不低于65分 4、cambridge proficiency/advanced test (cae)：均分在184分及以上，小项不低于169分 如果你对该课题感兴趣，且能满足学校对本（硕）阶段学习成绩和入学前英语语言成绩的要求，请尽快联系导师。 来信请附上你的英文简历、成绩单、英语语言成绩以及科研（论文和专利等）情况材料。 Dr. Enrico Marsili 邮箱：enrico.marsili@nottingham.edu.cn |
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