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/ computer vision and pattern recognition, including but not limited to biomedical applications Strong interest in applied machine learning, including but not limited to deep learning Experience utilising GPU
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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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paraganglioma driven by cell plasticity using spatial transcriptomics and machine learning.” High-risk neuroblastoma (NB) and malignant paraganglioma (PPGL) are neural crest–derived tumors with pronounced
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, integrative biology approach that utilizes human pluripotent stem cell based model systems, high throughput functional genomic screening and big data based machine learning, bridging the scales from genetics
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speech at the pinnacle of complexity. Like human babies, songbirds learn their vocalizations early in life from a social tutor. Numerous parallels to human speech learning, including analogous neural
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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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your research results in peer-review scientific publications and international conferences Disseminate your results through public engagement and social media Teach and supervise MSc and BSc students as
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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
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to learn and team spirit Strong sense of responsibility, organisational skills, high reliability and flexibility Experience in single cell/nuclei or spatial transcriptomics are preferable, but not required