Postdoc on Quantum Bayesian Optimization for Molecular and Material Design

1000scholars; Amsterdam; On-site; EUR 41,000 - 64,000; Full time

Job summary

University of Amsterdam seeks a Postdoctoral researcher to explore quantum Bayesian optimization for molecular and material design. The role focuses on combining quantum and classical computing to accelerate simulations and optimize properties, with collaboration across the MMD technology hub, SURF’s Quantum Computing division, and DIEP.

You will contribute to seminars and supervise theses, while advancing novel quantum ML pipelines.

Qualifications

• PhD in Quantum Computing or closely related field.
• Publications in international refereed journals are desirable.
• Strong communication and willingness to collaborate across disciplines.

Responsibilities

• Conduct original research at the intersection of Quantum Computing, ML, and Bayesian optimization.
• Design and model molecules and materials using hybrid quantum-classical pipelines.
• Publish results in peer‑reviewed journals and present at conferences.
• Collaborate with internal and external partners across hubs and labs.

Skills

Quantum Computing

Machine Learning

Statistical Inference

Atomistic Modelling

English proficiency

Education

PhD in Quantum Computing

Tools

Qiskit

Python

Job description

Postdoc on Quantum Bayesian Optimization for Molecular and Material Design

UvA - University of Amsterdam

Amsterdam, Netherlands

€ 3,706 - € 5,760 per month gross

In this pivotal postdoctoral position, you will explore and realize the potential of Quantum Computing to improve Machine Learning models to accelerate atomistic simulation and model molecular and material properties. You will focus on Quantum Optimization and in making the complexity of Quantum Circuits tractable, but you will be free and encouraged to propose and pursue novel directions. You will collaborate closely with the Quantum initiative of the Molecular and Material Design (MMD) technology hub, as well as with the Quantum Computing division of SURF and with the Dutch Institute for Emergent Phenomena (DIEP). You will be embedded in the Computational Soft Matter (CSM) Lab, a joint initiative of the Institutes of Informatics (IvI), Physics (IoP) and Molecular Sciences (HIMS).

All about this vacancy

This is what you will do:

• You are expected to conduct original and fundamental research at the intersection of Quantum Computing, Statistical Physics, Atomistic Modelling, Machine Learning and Bayesian Optimization methods.
• Designing and modelling novel molecules and materials with tailored properties requires exploring prohibitively vast combinations of elemental compositions and configurations. This project focuses on implementing combined Quantum and Classical Computing pipelines to accelerate atomistic simulations and optimize molecular and material properties. You will have a unique chance to put your expertise and creativity into practice, and you will be surrounded and supported by a rich environment with experts in Quantum Computing, Minimally Complex Models, Atomistic Modelling, etc.
• conducting independent research in quantum Bayesian optimization and its applications to molecules and materials, resulting in academic publications in peer-reviewed international journals, presentations at national and international conferences, etc.;
• contributing to internal seminars, symposia, and similar knowledge transfer activities, as well as their organization at the different affiliations related to the position;
• actively collaborating within the MMD technology hub, and with the Computational Chemistry group at HIMS, the Computational Science Lab at IvI, and experimental groups at HIMS and IoP.

You will also be provided with opportunities to be involved in:

• supervising Bachelor and Master theses in varied disciplines;
• contributing to research schools, teaching efforts, tutorials, guest lectures, etc.

What we ask of you

You are a resilient, creative researcher with strong technical knowledge. You feel comfortable working on unexplored topics with limited literature. You enjoy working across disciplines and you communicate well with diverse teams. You value and balance both independence and collaboration.

Your experience and profile

• PhD in Quantum Computing, Computational Chemistry/Physics, Machine Learning, Statistical Inference or affine fields;
• committed researcher, demonstrated by publications in international refereed academic journals and academic publishers;
• readiness to participate in interdisciplinary cooperation;
• professional command of English

It is a preference if you have experience in two or more of the following fields: Quantum Computing, Machine Learning, Statistical Inference, Atomistic Modelling.

Deadline 16 October