Postdoctoral Researcher in Quantum Machine Learning for Drug Development, Predictive Science Digital and Automation, Pharmaceutical Sciences, Biopharma R&D, Gothenburg, Sweden

AstraZeneca is a global science-led biopharmaceutical company. Within Pharmaceutical Sciences we develop the therapies of the future by connecting deep scientific expertise with modern modelling and data sciencehelping teams move faster and make better decisions from molecule to medicine.

The Predictive Science Digital and Automation (PSDA) department works at the interface of experimental science physical modelling and AI/machine learning to accelerate development across e.g. synthetic routes solid-state/formulation understanding and developmentability always with a focus on real-world impact for patients.

The opportunity

We are recruiting a Postdoctoral Researcher to lead a research project on how quantum machine learning (QML) with a focus on quantum kernel methods and learning in feature Hilbert spaces could improve prediction of chemical properties relevant to drug development

The project will investigate whether performance and data efficiency can be improved by using higher-accuracy quantum representations beyond standard DFT and by applying QML methods.

The position is embedded within the Wallenberg Center for Quantum Technologies (WACQT) which provides a rich scientific ecosystem with worldclass experts in both theoretical and experimental quantum technology offering a uniquely stimulating environment for advancing frontier research. You will join AstraZenecas Predictive Science Digital and Automation department and work closely with your academic supervisor benefiting from access to leading quantumcomputing expertise and hardware.

What you will do

In this postdoctoral role you will lead research and prototyping at the intersection of quantum computing quantum chemistry and machine learning. You will develop and evaluate QML models (with emphasis on kernel/feature-space viewpoints) design benchmarking experiments and metrics implement reproducible computational workflows and communicate results to both technical and non-technical stakeholders. You will work closely with colleagues in PSDA your academic supervisor and internal domain experts in chemistry and pharmaceutical development to keep the research grounded in high-value use cases. The work will involve simulations as well as implementations on real quantum hardware.

The academic supervisor is Professor Mats Granath at the Department of Physics University of Gothenburg. The workplace will be distributed between AstraZeneca and Chalmers campus both located in Gothenburg.

About WACQT

This position is supported by the Wallenberg Centre for Quantum Technology (WACQT) which receives funding from Knut & Alice Wallenberg Foundation. WACQT is a 12-year billion SEK initiative with the purpose of advancing Swedish academia and industry to the forefront of quantum technology and to build a Swedish quantum computer. WACQT is committed to promoting career development diversity and gender equality through networking and supporting activities.

Who you are

Essential requirements

• PhD in a relevant area (e.g. quantum machine learning quantum chemistry quantum computing computational physics or classical machine learning).
• Demonstrated ability to produce highquality research outcomes through rigorous computational work.
• Comfortable framing hypotheses building prototypes running careful evaluations and translating results into clear actionable conclusions.
• Strong experience with Python and modern scientific software practices (testing version control packaging reproducibility).
• Able to work constructively across disciplines and communicate clearly.

Desirable requirements

• Familiarity with one or more platforms such as Qiskit PennyLane Cirq or similar.
• Hands-on understanding of electronic structure methods and/or kernel methods in machine learning.
• Enjoys cross-functional collaboration and knowledge sharing beyond immediate specialty.

Were looking forward to reading your application!

Date Posted

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