Postdoctoral Researchers in AI-driven atomistic modeling and AI-accelerated cheminformatics

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Aalto University is where science and art meet technology and business. We shape a sustainable future by making research breakthroughs in and across our disciplines, sparking the game changers of tomorrow and creating novel solutions to major global challenges. Our community is made up of 16 000 students and 5 200 employees, including 450 professors. Our campus is in Espoo, Greater Helsinki, Finland. Diversity is part of who we are, and we actively work to ensure our community's diversity and inclusiveness. This is why we warmly encourage qualified candidates from all backgrounds to join our community. The School of Chemical Engineering is one of the six schools of Aalto University. It combines natural sciences and engineering in a unique way. We are looking for several Postdoctoral Researchers in AI-driven atomistic modeling and AI-accelerated cheminformatics to join the Otaniemi Center for Atomic-scale Materials Modeling (OCAMM), hosted by the Department of Chemistry and Materials Science (CMAT). The positions to be filled are part of a new project funded by Business Finland, " Materials AI for accelerated industrial R&D ", carried out in collaboration with leading companies in the chemistry and materials fields, as well as the Finnish national supercomputing center, CSC. In-house collaborations with experimentalists are also included in the project. The project is led by Prof. Miguel Caro (PI), Prof. Antti Karttunen, and Prof. Kari Laasonen, who will supervise the postdoc positions advertised here. We are seeking candidates with expertise in one or more of these areas: Machine-learning interatomic potentials; High-throughput automated workflows for atomistic simulations; Atomistic modeling of the structure of materials; Simulation of molecular diffusion and molecular interactions with surfaces and/or nanoporous media; Atomistic simulation of chemical reactions and/or thin-film growth; Data-driven cheminformatics for molecular modeling and/or design; Molecular dynamics simulations. Your background and expertise We are looking for candidates with a doctoral degree in computational chemistry, physics or materials science (including engineering doctoral degrees in these areas). Candidates with a doctoral degree in computational biology, applied mathematics, or applied computer science with applications in the areas of interest of the project (as listed above) will also be considered. Open positions have different specific requirements, but we are generally interested in candidates with following merits: Track record of scientific publications; Prior experience with high-performance computing (HPC) systems; Ability to interact with data and software programmatically (for example, familiarity with Python scripting) Strong command of written and spoken English, including the ability to effectively communicate and discuss ideas. You have previous practical experience in one or more of the following more specific topics: Machine learning interatomic potentials (GAP, NNP, MTP, ACE, MACE, etc.); Density functional theory studies of nanoparticles, surfaces, or bulk materials; Chemical reaction modeling and/or enhanced sampling (NEB, metadynamics, microkinetic modeling, etc.); Molecular dynamics simulations (LAMMPS, Gromacs, etc.); ML-based techniques applied to cheminformatics (e.g., high-throughput molecular data processing with SMILES). Expertise in the following areas is considered beneficial: scripting (Python, bash, etc.), high-performance programming (Fortran/C/C++, CUDA, etc.), ML libraries (sklearn, Pytorch, etc.). In your CV or motivation letter, you can also list other expertise that you may think is relevant for the advertised positions.

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