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pharmaceutical information to healthcare professionals or patients. We are seeking talented applicants, particulary those with a strategic vision for advancing the field of digital pharmacy, a deep understanding
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structuring their courses, integrating digital learning tools, and setting up course materials in Moodle. This role is essential for the revision of the MSc MTEC curriculum, ensuring smooth course
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experience in statistical analysis and implementing machine and deep learning models using Keras/TensorFlow and/or PyTorch. You have experience in collaborative coding, version control, and utilizing computer
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broad and deep exposure to the AI research landscape, innovation ecosystems, academic-industry collaboration, and science policy. Associates gain hands-on experience in science strategy, research
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to the eDIAMOND project’s goals (see Job description section above). Theoretical and practical experience and passion in at least one of the following topics: Machine Learning: Deep Learning, Federated Learning
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-Cas gene editing Pooled CRISPR screens Preparation of deep sequencing libraries for functional genomics Targeted protein degradation and induced proximity therapeutics Protein quantification and
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related field, has a deep knowledge of cell biology, demonstrated project management and communication skills, plus a keen interest in latest genomics technologies and their application in one of biology’s
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Biljecki (National University of Singapore). Your key responsibilities will be: 1. Co-developing the research of work package 1. This work includes, a.o.: Developing deep learning models for the project’s
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Mathematics, Statistics, Physics, or other quantitative discipline Solid programming skills Fluency in both spoken and written English is mandatory Ideal Qualifications Experience with deep learning frameworks
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eager to learn new science and technologies as well as contribute to the group through knowledge transfer of your expertise. We are interested in applications spanning fitness, sports, affective science