AI for autonomous molecular discovery PostDoc positions and fellowships

This postdoctoral position focuses on developing AI methods for bionanoscience, specifically AI for experimental science and multimodal data analysis, as well as AI for autonomous molecular and materials discovery. The successful candidate will work in Professor Dame Molly Stevens’s lab at the Kavli Institute for Nanoscience Discovery, University of Oxford, contributing to biomaterials, biosensing, diagnostics, tissue engineering, and related areas through machine learning and computational workflows.

The Postdoctoral Fellow in Agentic AI for Scientific Discovery at the University of Waterloo and Vector Institute will lead research on developing AI agents that retrieve, reason over, and interact with scientific data and software, focusing on chemistry. The role involves creating benchmarks, mentoring students, collaborating with industry, and publishing at top AI conferences, with a competitive salary and access to H200 GPU resources.

This postdoctoral position at Chalmers University of Technology focuses on developing novel artificial intelligence and machine-learning methods for accelerated materials discovery. The project combines generative AI, active learning, first-principles simulations, and machine-learning potentials to identify new multicomponent optoelectronic materials for energy-conversion applications. The successful candidate will contribute to building an autonomous materials-discovery framework capable of navigating vast compositional spaces while optimizing optical performance, stability, and sustainability. Responsibilities include developing ML models, designing active-learning workflows, performing large-scale computational screening, analyzing structure–property relationships, publishing results, and contributing to open-source scientific software. The role also involves supervision of master's and PhD students and opportunities to contribute to teaching at undergraduate and master's levels.

The Postdoctoral Researcher in AI-Enabled Autonomous Materials Discovery at Argonne National Laboratory focuses on developing an AI-enabled autonomous experimentation platform to accelerate materials discovery, particularly in organic crystal growth and structure-property relationships. The role integrates robotic experimentation, advanced characterization techniques, and machine learning methods to enable high-throughput screening and data-driven insights. Candidates will collaborate with interdisciplinary teams, leveraging Argonne’s world-class facilities to innovate in materials science and automation.

The Miao Lab at UNC-Chapel Hill seeks a Postdoctoral Research Associate to develop AI-driven computational methods for drug discovery and biomolecular simulations. The role focuses on accelerating molecular recognition and cellular signaling studies, with collaboration opportunities on therapeutically important targets like GPCRs and RNA.

Two PhD scholarships at DTU Energy focus on battery modelling and fabrication, combining computational AI‑enhanced multiscale simulations with experimental synthesis and characterization of potassium–sulfur solid‑state electrolytes. Candidates will work in a collaborative, interdisciplinary environment to investigate interfacial degradation and self‑healing mechanisms, contributing to next‑generation sustainable energy storage.

The VHIR-MSCA-PF-2026.003 Postdoctoral Fellowship focuses on precision modelling of Familial Hypomagnesemia with Hypercalciuria and Nephrocalcinosis (FHHNC) using patient-derived kidney organoids to uncover disease mechanisms, phenotypic variability, and sex-specific aspects, while evaluating therapeutic candidates through drug repurposing. The successful candidate will join the Kidney Pathophysiology Research Group at Vall d'Hebron University Hospital in Barcelona, working within a multidisciplinary environment that integrates stem cell technology, genome engineering, and translational nephrology to advance understanding of this ultra-rare renal tubulopathy and develop novel precision medicine strategies.

A postdoctoral position at Chalmers University of Technology focusing on developing generative models to accelerate molecular dynamics simulations. The role combines machine learning with molecular science, offering collaboration opportunities and access to advanced computing facilities.

Postdoctoral Research Associate at Northeastern University to develop AI-driven molecular dynamics simulations for drug discovery across multiple disease areas. The role involves algorithm development, data analysis, collaboration with medicinal chemists, manuscript and grant writing, and training junior team members, requiring a Ph.D. in Chemistry, Computer Science or related fields and expertise in MD simulation and HPC.

This PhD position at the University of Liverpool focuses on combining 3D generative modelling, structural informatics, and chemical insight to address challenges in drug discovery. The research aims to investigate how 3D molecular novelty impacts synthesisability and develop methods to mitigate this loss. The project will integrate conditioning constraints from high-quality informatics sources and validate findings through case studies targeting well-characterized systems. It's part of the EPSRC Centre for Doctoral Training in Digital and Automated Materials Chemistry.