Candidates

The NCCR SPIN Industry Internship Mobility Grants are designed for Master’s students, PhD students and postdocs within the NCCR SPIN network who want to enhance their scientific profile by conducting their study project in a hosting team at QuantumBasel on one of the available topics, while still being enrolled at their home institution.

The minimum stay is 3 months for a maximum of 9 months. If all parties agree, the internship may be extended.

Award

Up to 1’600 CHF per month paid directly to the awardee for a stay of up to 9 months. The grant is cofunded by NCCR SPIN and QuantumBasel.

Application procedure

Applications are evaluated on a rolling basis

Applications are to be submitted to Maria.longobardi@unibas.ch

Required documents:

• CV (curriculum vitae)

• Motivation letter including research goals

• Letter of acceptance from the Quantum Basel supervisor (please reach out via careers@quantumbasel.com)

• Letter of support from the NCCR SPIN supervisor

All documents should be in English.

Important eligibility requirement:

Applicants must already be members of the NCCR SPIN network and actively contributing to one of the NCCR SPIN projects.

Applications from individuals outside the NCCR SPIN network will not be considered.

Selection

• Selection is made on a competitive and rolling basis. The selected candidates must confirm acceptance of the grant within 2 weeks; otherwise, the award may be attributed to another candidate.

• Selected candidates are asked to begin their stay in the host team at QuantumBasel within 4 months.

• The number of grants depends on the NCCR SPIN budget.

• Funding might be prorated depending on the length of stay in certain cases.

• NCCR SPIN will cover the travel costs for one round trip to the home institution for the awardee, up to 300 CHF.

• The awardee is responsible for paying accommodation, visa, and other related fees.

Potential topics available at QuantumBasel (non exhaustive list)

• Time series forecasting: applying quantum and classical models to forecast time series

• Quantum generative AI: enhancing GANs and other generative approaches with quantum algorithms

• LLM fine-tuning and agentic AI workflows: fine-tuning LLMs for specific applications and integrating them with AI agents

• Quantum machine learning: applying quantum and quantum-inspired algorithms in machine learning

• Molecular structure/dynamics: predicting the structure/dynamics of (bio-)molecules with greater accuracy and efficiency

Contact

If you have any questions, feel free to contact Maria Longobardi (Maria.longobardi@unibas.ch)