PhD position

AI Methods for Quantum Algorithms in Chemistry

Ref. 2025_038

We invite applications for a fully funded PhD position developing algorithms at the intersection of artificial intelligence and quantum computing for molecular and reaction modeling. The PhD fellow will be part of the LowDataML European ITN (Innovative Training Network) and will work at IBM Research Europe - Zurich, while being enrolled at Eindhoven University of Technology (TU/e). The projects will be jointly supervised by IBM Research Scientists and a TU/e Professor to advance AI-guided quantum workflows on today’s and tomorrow’s hardware.

What you will do

• Design AI methods that assist in the selection, control, and assessment of quantum algorithms for quantum-chemical problems (electronic structure, reaction-energy profiles, spectral properties in catalysis and complex environments).
• Integrate AI models into hybrid quantum-classical workflows to identify subproblems suitable for quantum treatment, configure realistic quantum simulations, and analyze outputs.
• Leverage chemistry foundation models as feature extractors and priors to encode molecules, conformers, and reaction conditions into high-quality representations for downstream ML components interfaced with quantum algorithms.
• Investigate three key AI-for-quantum strategies:
  1. Learning-based active-space and embedding choices in multireference or fragment-based schemes (assigning parts of a system to quantum resources).
  2. Data-driven construction and adaptation of circuits for variational and sampling-based quantum diagonalization (e.g., SQD, SqDRIFT) on noisy hardware.
  3. ML-assisted parameter initialization and post-processing to accelerate convergence and sampling.
• Generate and curate synthetic training data from validated quantum-chemistry calculations and circuit simulations to address data sparsity in chemistry.
• Benchmark rigorously against established quantum-chemical baselines and analyze scaling and robustness.
  
  

Required Qualifications

• Enrollment eligibility for a PhD: a Master’s degree (or equivalent) in Computer Science, Electrical Engineering, Physics, Chemistry, Applied Mathematics, or a closely related field.
• Hands-on experience with quantum computing (e.g., Qiskit or other SDKs): circuit design/optimization; execution on simulators and/or real devices.
• Foundational knowledge in AI/ML (optimization, model training, regularization, metrics) and demonstrable practice applying ML to scientific or engineering problems.
• Strong Python skills and sound software practices (Linux, git/GitHub, testing, reproducibility).
• Familiarity with experiment orchestration, containers, and CI/CD.
• Clear communication in English and a structured, collaborative working style.

Preferred Qualifications

• Solid background in algorithm development and linear algebra.
• Experience with electronic- structure methods (e.g., DFT, MP2/CC, multireference approaches, embedding) or molecular spectroscopy.
• Exposure to variational and sampling-based quantum algorithms (e.g., VQE, SQD) and noise-aware circuit optimization.
• Experience with chemistry foundation models, graph neural networks, or transformers for molecular/reaction representations.
  
  

Location, Timing, and Format

• Location: IBM Research Europe - Zurich, Switzerland; research visits at TU/e as appropriate.
• Duration: Typically, 4 years.
• Supervision: Joint-supervision by researchers from AI and Quantum team at IBM Research Europe - Zurich and TU/e.
  
  

Diversity

IBM is committed to diversity at the workplace. With us you will find an open, multicultural environment. Excellent flexible working arrangements enable all genders to strike the desired balance between their professional development and their personal lives.

How to apply

Please submit your CV including contact information for two or three references. We encourage candidates to also share a 3-minute video, in which they introduce themselves, as well as highlight their motivation and expertise. The video is not mandatory.

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