908 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"FEUP" positions at Nature Careers
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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development. The candidate will reinforce expertise in digital methods in construction, focused on project management. They are expected to: (i) Conduct research in: Construction informatics, BIM, data science
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oncology and health policy by providing rigorous training in the use of big data, advanced biostatistical methods, and econometric approaches, paired with dedicated mentorship from experts to advance
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Assistant professor in Analysis and Modelling of Nitrogen Cycling, Losses and Emissions in Croppi...
research sections with around 320 highly dedicated employees, of which approximately 50% are scientific staff. More information can be found here . We believe in encouraging inclusion, acceptance, and
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Assists in performing investigative research under the mentorship in a particular area(s) of study. Publishes and presents data in a timely manner. Works collaboratively with other investigators
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, Urban & Rural Planning, Environmental Science & Engineering, Chemical Engineering & Technology, Mechanical Engineering, Transportation Engineering, Civil Engineering, Data Science & Big Data Technology
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will be part of a research environment focusing on integrating multi-source satellite remote sensing data and developing novel algorithms to quantify agroecosystem variables for environmental
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/information indicating the expected defense date Transcript of all modules and results from university-level courses taken List of publications Names and contact details of two academic referees Early
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to the advertised research topic and/or project, including how your background, interests, and career goals align with its objective PhD diploma or a letter/information indicating the expected defense date Transcript
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Postdoctoral Researcher Position in Ecological Knowledge-Guided Machine Learning at Aarhus Univer...
on “Integrating AI into Aquatic Ecosystem Models to Decode Ecological Complexity” funded by Villum Fonden. Within that project, the focus is on exploring novel ways to infer information from environmental data