Two Tenure Track Assistant/Associate Professors in AI4Science and AI4Materials

The Departments of Energy Conversion and Storage (DTU Energy) and Applied Mathematics and Computer Science (DTU Compute) seek two exceptional Tenure Track Assistant/Associate Professors for a new cross-departmental initiative on AI4Science, specifically AI4Materials. The Tenure Track Professors will be anchored in the two large-scale and long-term Pioneer Centers for Accelerating P2X Materials Discovery (CAPeX) and Artificial Intelligence (P1) and have joint affiliation at both departments (see descriptions of the departments and pioneer centers below).

The new AI4Materials initiative offers a unique opportunity to develop your career in a highly dynamic international and bilingual environment, bridging fundamental method development in computer science and AI with scientific approaches and technological challenges in materials physics and chemistry in sustainable energy materials and biomolecular applications. In close collaboration with the departments and the pioneer centers, you will also be jointly responsible for developing a new curriculum and educational strategy for the initiative. 

Responsibilities and qualifications

We value diversity and encourage applications from individuals of diverse backgrounds to submit their applications. We strongly believe that fostering diversity in the research environment enhances creativity and promotes transdisciplinary collaboration, ultimately contributing to the successful execution of excellent research and innovation. We are striving to build a vibrant team that reflects our commitment to excellence, diversity, and interdisciplinary collaboration.

To be considered for the position you must have earned a PhD degree in computer science, physics, chemistry, chemical engineering, materials science, or a related area and have a documented record of excellence in scientific research, including publications in top-ranked and field-relevant journals and/or presentations at significant conferences. Documented experience in teaching and supervision is also expected.

Furthermore, we imagine that you have a strong theoretical background and substantial research experience in computational or experimental approaches for designing, discovering or characterizing advanced materials, preferably combined with a strong expertise in AI-accelerated materials discovery, e.g., machine learning, graph-based models, generative AI, data-driven modeling, or workflows. Specific areas of interest include, but are not limited to, closed loop materials synthesis and modeling with atomic, multi-scale, and machine learning approaches to understand complex material. 

In addition, the ideal candidate for each of the two positions should have the following expertise:

• AI4Science: Experience in method development and application of machine and deep learning for computational materials design, discovery, or characterization, e.g., graph-based models, generative AI or foundational models, time-series, segmentation, or multi-fidelity and multi-objective optimization. Experience working with inorganic and/or organic materials, multi-modal/sourced/fidelity or experimental datasets, and (autonomous) workflows bridging simulations and experiments will be viewed favorably.
   
• AI4Materials: Experience with the synthesis of inorganic energy materials and nanoparticles, as well as working with AI-orchestrated self-driving laboratories with (semi) closed-loop materials discovery, e.g., Bayesian Optimization, autonomous analysis of patterns or spectral data. Experience with predictive control of the synthesis robotics and reaction conditions and materials innovation in energy systems, emphasizing the design and synthesis of complex inorganic/organic architectures, functional surfaces, or nanoparticles, e.g., for electrocatalysis or materials degradation.

Application procedure 

Your complete online application must be submitted no later than 21 April 2025 (23:59 Danish time).

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