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Field
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foundations of feature learning in neural networks. Engage in the life of the CSD, including participating at the weekly seminars. Present results internally and at international venues; Qualifications PhD in
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this project is how to effectively prioritize the millions of unknown biosynthetic gene clusters and metabolite features for the discovery of new antimicrobials through predicting structural and functional
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technological research clusters in the fields of materials and nanomaterials, biotechnology, microelectronics and life sciences. Its work is geared towards applied research and innovation to meet market needs
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. Piekarski ) This research initiative is dedicated to the theoretical and experimental exploration of the evolutionary dynamics of various molecular targets, including chiral molecules and molecular clusters
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structural development over time at the group, sub-group, and individual level (e.g., using normative modelling and clustering approaches to parse heterogeneity). The candidates will further have the
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of PhD students in the project Profile We are seeking highly motivated candidates with strong achievements and a proven track record. Applicants must have: A PhD in physics, chemistry, materials science
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QUALIFICATIONS PhD in Biochemistry, Molecular Biology, Plant Biology, or a related field; Proven experience in the field of plant medicinal natural product biochemistry; Proficiency in molecular cloning, protein
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& Desirable Criteria Essential A PhD in physics, electrical engineering, or other relevant subjects. Candidates must have submitted their doctoral thesis before starting in post. The ideal candidate will have a
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Bioinformatics, Molecular Biology, Biochemistry, Computer Science, or related field Special Skills, Knowledge, and Abilities: Required: Candidates must hold a doctoral degree (PhD, MD, or equivalent). Applicants
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Python and Pytorch as well as deployment on GPU clusters. A strong publication record in graph neural networks, geometric deep learning, representation learning, machine learning–based molecular/materials