Postdoctoral researcher in quantum algorithms and optimization for Life Science applications
Core
Developing hybrid quantum-classical algorithms for bioinformatics, breeding, biomaterial synthesis, and cellulose-processing enzyme design using near-term and medium-to-long-term quantum methods.
Role type
Postdoctoral researcher in quantum algorithms and optimization
Builds
Research outputs (publications) and novel quantum methodologies for life science applications
Domain
Quantum computing / Computational biology / Genomics / Materials science
Deliverable
production ML models
Required skills
Quantum computing theory, quantum walk algorithms, variational quantum algorithms, adiabatic quantum computation, quantum machine learning, algorithm optimization, hybrid quantum-classical algorithm design, NISQ-era deployment, expressivity analysis, training stability, scalability assessment, resource requirements evaluation, classical baseline benchmarking
Preferred skills
Teaching experience, PhD supervision experience, familiarity with bioinformatics, breeding, biomaterial synthesis, or cellulose-processing enzyme design
Technologies
VTT Q50, Aalto Q20, commercial quantum platforms
Responsibilities
Conduct independent research in quantum algorithms and quantum information, develop hybrid quantum-classical algorithms for domain-specific problems, publish high-quality research results, supervise PhD students, participate in teaching activities
Seniority
Postdoctoral researcher, independent researcher with collaboration
