28 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" PhD positions at Nature Careers in Germany
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(stamped arrival date of the university central mail service or the time stamp on the email server of TUD applies), preferably via the TUD SecureMail Portal https://securemail.tu-dresden.de by sending it as
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research group “Machine Learning for Biomedical Data” led by Prof. Dominik Heider and is embedded in the DFG-funded Collaborative Research Centre 1748, Principles of Reproduction. The CRC 1748 involves
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PhD student (m/f/d) in the field of chemistry, chemical engineering, materials science or comparable
polyoxometalates Using suitable characterization methods to characterize the synthesized materials Using machine learning tools to tune the synthesis parameters in a feedback loop and enhance the properties
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assessment of chemical plants using HAZOP analysis Use of process modeling and simulation to enhance quantitative assessments Use of machine learning to support HAZOP discussions with the aim of obtaining a
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protocols to characterize both cellular and vascular properties of the TME. The approach will be validated using a combination of in silico models, computer simulations, and in vitro experiments using tumor
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sciences or similar Strong interest in single-cell nucleus RNA-seq techniques / Perturb-Seq / Crop-Seq Willingness to learn computational biology/bioinformatics High motivation for scientific work and
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approaches Encouragement to pursue own research ideas Opportunity to develop an independent scientific profile Possibility to acquire funding to support future projects REQUIREMENTS: Highly motivated and
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degree in Biology, Biochemistry or Chemistry Experience in protein biochemistry, molecular biology and/or cell biology Willingness to learn new methods and technologies in an international and