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validation in rodent migraine models Close collaboration with computational protein engineers and clinical researchers Data analysis, manuscript preparation, and supervision of students where relevant
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generation Developing and optimizing generative models for de novo minibinder design Integrating structural biology data into AI pipelines for receptor–ligand interaction modeling Fine-tuning large protein
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here . Further information Further information may be obtained from Prof. Marcel A.J. Somers (majs@dtu.dk ). You can read more about Department of Civil and Mechanical Engineering at www.construct.dtu.dk
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at DTU here . Further information Further information may be obtained from Senior Researcher Dennis Christensen, dechr@dtu.dk , +45 20961946. You can read more about our department DTU Energy at
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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
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advanced data science, using state-of-the-art human stem cell models to uncover previously unrecognized environmental risk factors for Parkinson’s Disease. You will, in close collaboration with a PhD student
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for the optimal candidate later dates may be negotiated. You can read more about career paths at DTU here . Further information For further information about the research at SurfCat at the Department of Physics
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Aarhus University with related departments. Contact information Before applying or for further information, please contact: Associate Professor Aurelien Dantan, +4523987386, dantan@phys.au.dk . Deadline
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can read more about career paths at DTU here . Further information Further information may be obtained from professor Poul Sørensen, email posq@dtu.dk , mobile phone +45 2136 2766. You can read more
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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