We have an opening for a Postdoctoral Researcher to conduct research in cheminformatics and materials informatics. You will be part of an interdisciplinary team of computer scientists, materials scientists, and chemists applying existing and developing new machine learning techniques to organic chemistry to accelerate the design and development of materials with targeted properties. This position is in in the Functional Materials Synthesis and Integration Group of the Materials Science Division.
In this role you will
Apply data science methods to chemistry problems to aide in the discovery of new molecular and polymeric compounds with targeted properties.
Elucidate via machine learning statistical techniques the underlying chemistry-function relationships to guide improvements in molecular design and synthesis.
Evaluate existing methods and devise improvements for evaluating "synthesizability" of new model-derived materials.
Contribute to and actively participate in the development of novel concepts applying machine learning to chemistry and materials science to meet sponsor needs in appropriate national security areas.
Pursue independent but complementary research interests and interact with a broad spectrum of scientists internally and externally to the Laboratory.
Collaborate with scientists in a multidisciplinary team environment to accomplish research goals.
Maintain and establish laboratory protocols.
Document research; publish papers in peer-reviewed journals, and present results within the DOE community and at conferences.
Perform other duties as assigned.
PhD in chemical engineering, computer science, materials science, mathematics or related field.
Experience in one or more higher-level programming languages such as Python, Java/Scala, Matlab, R or C/C++.
Experience and fundamental knowledge of developing and applying algorithms in one or more of the following Machine Learning areas/tasks: deep learning, unsupervised feature learning, zero- or few-shot learning, active learning, reinforcement learning, multimodal learning, ensemble methods, scalable online estimation, and probabilistic graphical models.
Experience with one or more deep learning libraries such as TensorFlow, PyTorch, Keras, Caffe or Theano.
Ability to develop independent research projects and publish in peer-reviewed literature.
Proficient verbal and written communication skills as reflected in effective presentations at seminars, meetings and/or teaching lectures.
Initiative and interpersonal skills with desire and ability to work in a collaborative, multidisciplinary team environment.
Qualifications We Desire
Knowledge of or experience in chemistry or materials science
Experience with RDKit and/or Deepchem
Experience with natural language processing (NLP)
Internal Number: 3743990000022329
About Lawrence Livermore National Laboratory
For more than 60 years, the Lawrence Livermore National Laboratory has applied science and technology to make the world a safer place.Livermore’s defining responsibility is ensuring the safety, security and reliability of the nation’s nuclear deterrent. Yet LLNL’s mission is broader than stockpile stewardship, as dangers ranging from nuclear proliferation and terrorism to energy shortages and climate change threaten national security and global stability. The Laboratory’s science and engineering are being applied to achieve breakthroughs for counterterrorism and nonproliferation, defense and intelligence, energy and environmental security.
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