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xetra:mrk
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intermolecular.com
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intermolecular.com
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Aug 14th, 2020 12:00AM
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Simulations Internship
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Open
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Engineering
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San Jose, CA, United States
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San Jose
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CA
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USA
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Feb 12th, 2020 12:00AM
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Key responsibilities:- Participate in simulations projects; the primary project may be chosen depending on intern’s qualifications, and may include:Modeling kinetics of the process of atomic layer deposition (ALD) and developing workflows to extract the parameters necessary for predictive ALD kinetics modeling from experiment;Using machine learning to generate potentials for classical molecular dynamics (MD) from ab-initio MD dataOther simulations projects using first-principles and device modeling software, and/or machine learning tools.- Collaborate with Intermolecular research teams on customer and internal R&D projects and workflow development as necessary.Qualification requirements:- At least two years of college preparation are required (candidates holding M.S. and demonstrating a solid progress towards Ph.D. are particularly encouraged to apply).- Solid understanding of key thermodynamic concepts relating to materials stability, chemical transformations, and kinetics of chemical reactions is required.- Solid programming skills in at least one programming language are required.- Knowledge of calculus and partial differential equations (PDEs) is required.- The candidate should have good communication skills and a strong drive to deliver in a high-pace collaborative environment.Other desired qualifications:- Understanding of solid state physics, fundamentals of materials science, and semiconductor physics is strongly preferred.- Familiarity with python is preferred (proficiency and at least one year experience using python, numpy, and scipy is strongly preferred).- Familiarity with numerical solution methods for PDEs (and their stability) is strongly preferred.- Familiarity with the physics of diffusion and processes defining the ALD window (chemisorption, physisorption, condensation, desorption, decomposition) is strongly preferred.- Experience using Tensorflow or other machine learning tools / frameworks is strongly preferred.- Experience in modeling ALD kinetics, diffusion processes (both continuum and molecular flow regimes), and/or kinetics of chemical reactions is preferred.- Experience using DFT (in particular VASP) and/or MD is preferred.- Familiarity with gas flow modeling tools is a plus.- Experience using linux, bash scripting, job schedulers and/or Jupyter server setup is a plus.- Candidates with experience in the following areas are encouraged to mention it, such an experience may be a plus but is not required: NEB calculations, calculations of surface/interface energetics and reconstruction, modeling of ferroelectric materials, amorphous materials, multiscale modeling, TCAD modeling (in particular Ginestra), thermodynamic modeling (including CALPHAD, cluster expansion and other DFT-based methods).The above statements are intended to describe the general nature and level of work being performed by people assigned to this classification. They are not to be construed as an exhaustive list of all responsibilities, duties, and skills required of personnel so classified. All personnel may be required to perform duties outside of their normal responsibilities from time to time, as needed.All qualified applicants will receive consideration for employment without regard to race, sex, color, religion, sexual orientation, gender identity, national origin, protected veteran status, or on the basis of disability.
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Aug 14th, 2020 02:07PM
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Aug 14th, 2020 02:07PM
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Merck KGaA
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