Research Fellow in Atomistic Machine Learning
vor 4 Wochen
Postdoctoral Researcher in Atomistic Machine Learning
We are seeking a dedicated Postdoctoral Researcher to become a part of the AI-Based Material Science team within the Physics Department at Technical University Munich.
In this role, you will have the opportunity to contribute to the advancement of sustainable and digital innovations by leveraging artificial intelligence (AI) in the fields of physics, chemistry, and materials science. Our mission is to enhance atomistic materials science through machine learning, providing insights into fundamental atomic processes and expediting the development of new materials.
RESPONSIBILITIES AND OBJECTIVES
You will develop, maintain, and implement machine learning methodologies for atomistic challenges in materials science. The range of techniques and problem domains includes machine-learned interatomic potentials, multimodal regression, generative models, and Bayesian optimization. Additionally, you will perform electronic structure theory calculations (such as density-functional or Green's function theory) to produce data for machine learning applications and analyze materials properties and phenomena at the atomic level. You will oversee extensive supercomputer simulations alongside AI algorithms and data analysis tools, lead the development of machine-learning software, and mentor team members.
QUALIFICATIONS AND ASPIRATIONS
We invite applications from candidates holding a PhD in computational physics, chemistry, or materials science who are enthusiastic about applying machine learning in the natural sciences. Prior experience in machine learning is essential. We are looking for individuals who are passionate about coding, scripting, and data analytics, and who are eager to explore the frontiers of computational materials design. This position demands innovative thinking and programming skills, along with technical proficiency in materials simulations and a comprehensive understanding of electronic behaviors in materials. We also value candidates who are willing to travel, teach, mentor, collaborate, and effectively communicate scientific concepts.
BENEFITS
Within the AI-Based Materials Science group, led by Prof. Patrick Rinke, we are at the forefront of electronic structure theory and machine learning, pursuing groundbreaking applications for future technologies. Our team is diverse and interdisciplinary, comprising members with various expertise and backgrounds. You will receive training in machine learning applications from seasoned developers, connect with our global network of collaborators, participate in scientific conferences, assist in organizing research workshops, and engage in academic and diversity outreach initiatives. We are committed to supporting your development into a competitive and international career profile. You will also be integrated into the materials science ecosystem at TU Munich, collaborating with excellence clusters and institutes focused on data science and atomistic modeling, and become part of a vibrant community at the intersection of AI research, physics, materials science, and renewable energy technologies.
The position is suitable for individuals with disabilities. Candidates with disabilities will be given preference when qualifications are essentially equal.
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