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Field
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) years of related work experience. Experience developing machine learning methods. Experience writing, submitting, and editing peer reviewed publications. Expertise in computer science applications
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generation of scientists and build a workforce equipped with expertise in integrating advances in biomedical engineering, technology, and Artificial Intelligence (AI) and Machine Learning (ML) methods
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experiments in the field of neuroscience, computational neuroscience and biologically plausible machine learning tools. Duties: Analyze data and developing biologically plausible machine learning frameworks
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. Additional background in renewable energy, surface science, catalysis, and/or machine learning. Strong programming skills in Python and some exposure to machine learning. Ph.D. in Materials Science, Physics
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record of peer-reviewed publications. A background is required in computer programming (including Julia and/or C/C++), applied mathematics and statistics. Please upload your application materials via
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are also developing novel machine learning methods to improve risk gene prediction and variant interpretation. This role will focus on the analysis of large-scale human genetics, scRNAseq, and proteomics
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plan that supports you and your family’s wellbeing and allows you to be a part of the life of a vibrant and active college campus. To learn more, go to Baylor Benefits & Advantages. Explore & Engage
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media Scientific machine learning Excellent programming skills in Python and C++ and prior experience with High Performance Computing Strong written and oral communication skills Appointment terms and
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must have the following essential skills: Strong background in plant breeding and genetics Strong computer skills and proficiency in R or other programming languages Outstanding interpersonal and
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acids, ligands), coarse-grain and polymer model development, multiscale modeling, molecular simulation code/software (e.g., LAMMPS, GROMACS), machine learning. Prior experience with applying simulations