Doktorand i AI-baserad design av tvinnade hybridnanotrådar
Core
PhD research in AI-driven design of twisted hybrid nanowires, combining computational protein design with experimental molecular biology and materials characterization.
Role type
PhD researcher (Postdoc level)
Builds
Novel twisted hybrid nanowires via AI-guided protein engineering and experimental validation
Domain
Biotechnology / Computational Biology / Materials Science
Deliverable
production ML models | product features
Required skills
Linux and Python for scientific data analysis, protein design platforms (RFdiffusion, ProteinMPNN, AlphaFold), high-throughput protein expression and purification, molecular biology and protein biochemistry, inorganic chemistry and mineralization processes, materials characterization methods
Preferred skills
Cross-disciplinary research experience, scientific writing and grant applications
Technologies
RFdiffusion, ProteinMPNN, AlphaFold, Linux, Python, GPU clusters
Responsibilities
Develop and apply modern computational platforms for protein design; execute high-throughput experimental workflows for protein expression, purification, and functional characterization; characterize inorganic materials; collaborate in interdisciplinary environments involving computational biology and materials science
Seniority
PhD candidate (4-year program)