60 web-development "https:" "https:" "https:" "Newcastle University" PhD positions at KU LEUVEN
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primarily expected to prepare a comparative law doctoral thesis in a functional area of law. The appointed person will primarily conduct academic research, both their own doctoral research and research within
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holder is primarily expected to prepare a PhD thesis on private economic law. The appointed person will primarily conduct scientific research, both independent research and within the framework
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activities take place in Leuven (supporting professors in preparing their classes, helping to prepare exams, teaching tutorials, supervising essays and theses, coaching moot courts); some take place in Hasselt
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environment, with the utmost respect for academic freedom, commitment, critical thinking and personal development. For more information please contact Prof. dr. Tim Opgenhaffen, mail: tim.opgenhaffen
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to antibiotics are rising. This doctoral research project aims for localized delivery of beneficial microbiomes to support robust healthy biofilm development. The project encompasses (i) electrospinning and
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social security law in its relation to national competition law and EU internal market rules. The candidate is supposed to prepare a decent research proposal as well as to prepare a doctoral thesis on the
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@kuleuven.be or Prof. dr. Frank Verbruggen, mail: frank.verbruggen@kuleuven.be.In case of problems with the online application, please contact solliciteren@kuleuven.be . Where to apply Website https
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application, please contact solliciteren@kuleuven.be . Where to apply Website https://www.kuleuven.be/personeel/jobsite/jobs/60637164?hl=en Requirements Research FieldJuridical sciencesEducation LevelMaster
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closed earlier in case a suitable candidate has been found. Where to apply Website https://www.kuleuven.be/personeel/jobsite/jobs/60644669?hl=en Requirements Research FieldCommunication sciencesEducation
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Infrastructure? No Offer Description The PhD candidate will work on the development of advanced statistical and machine learning methods for time series prediction, with applications mainly in the field of traffic