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intelligence, machine learning, big data and network analysis, computational and Bayesian methods, are encouraged to apply. Minimum Qualifications PhD in Statistics or closely related fields with documented
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. Developing workflows and machine learning algorithms to accelerate catalyst design (optional). Group: Atomistic & Molecular Modelling for Catalysis Group Requirements Specific Requirements PhD in Chemistry
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; experience with machine learning is a plus Demonstrated record of peer-reviewed publications Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork Preferred Qualifications
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design, and/or machine learning in the context of integrated photonics. We are looking for someone who wishes to work theoretically in this field, while still maintaining close contact with experiments
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groups working towards a common goal. For this postdoc project, we seek a dynamic and motivated candidate with an interest in computational electromagnetism, inverse design, and/or machine learning in
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positions in High-performance computation and machine learning, Chemical Physics, Physical Chemistry, Nano-science, Biophysics, Statistical Physics and Complex Systems. The positions are normally for one year
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and Nanoscience (MC2) . We offer a newly renovated lab space and a state-of-the-art nanofabrication facility. About the research project The aim of the project is to develop a hybrid quantum system
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for you! About us The Hybrid Quantum Systems Laboratory is a new laboratory at Chalmers in the division for Quantum Technology at the department for Microtechnology and Nanoscience (MC2) . We offer a
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publications. Desired: Experience in nanopore and/or other single-molecule experiments and their interpretation Coding skills for advanced data analysis, machine learning, kinetic modeling, etc. Nanofabrication
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and Machine Learning for Materials Discovery. The NanoScience Technology Center (NSTC) at the University of Central Florida (UCF) is a multidisciplinary research center established in 2004 with