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Field
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experience in deep learning frameworks (TensorFlow/PyTorch) Experience with large-scale genomic/proteomic datasets and machine learning applied to biological sequences Knowledge of phylogenetics, protein
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activities. Qualifications: Ph.D. in Bioinformatics, Computational Biology, Computer Science, Genomics, or a related field. Strong background in machine learning, particularly deep learning and natural
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skills in machine learning, deep learning, and advanced statistics for processing complex data. Urban Health Principles: Familiarity with urban planning principles centered on health (active mobility
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50 Faculty of Life Sciences Startdate: 01.05.2025 | Working hours: 20 | Collective bargaining agreement: §48 VwGr. B1 lit. b (postdoc) Limited until: 30.04.2031 Reference no.: 3736 Explore and teach
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Bioinformatics and/or Cheminformatics. Experience working in computational drug discovery, particularly multiscale applications. Experience working in Python and Bash. Understanding of machine/deep learning topics
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). Team player and great collaborator Strong interest in interdisciplinary work at the interface between dementia/ neurodegeneration, modeling, and machine learning Prior experience in deep learning, or/and
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informed neural networks (PINN) and explainable machine learning (EML) frameworks; experience in related technologies including large-scale data analysis, deep learning, Python, PyTorch; and the ability
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both. Ideally, you bring strong technical expertise and the curiosity to work across modalities and domains. Your responsibilities Design and implement machine learning and deep learning pipelines
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). Team player and great collaborator Strong interest in interdisciplinary work at the interface between dementia/ neurodegeneration, modeling, and machine learningPrior experience in deep learning, or/and
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industrial stakeholders, and we have ongoing collaborations with Fujifilm Diosynth, Opentrons, Lonza and Neochromsome. In collaboration with OccamBio Ltd, we aim at designing deep learning models to engineer