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Details Title Postdoctoral Fellowship in Reinforcement Learning, Probabilistic Methods, and/or Interpretability School Harvard John A. Paulson School of Engineering and Applied Sciences Department
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states, charge density waves, superconductivity, and quantum magnetism - Kagome materials and superconducting hydrides - Machine learning interatomic potentials (MLIPs) and data-driven atomistic
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protein coding genetic association data with functional and machine learning-derived features 4. Developing methods to characterize the genetic architecture of autism Salary and Benefits This position is
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development spanning areas such as optimization, Fourier analysis, numerical linear algebra, statistics, machine learning, and high-performance computing for one or more of the following: (1) reconstruction
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are expected to have a Ph.D. in theoretical particle physics or related areas prior to the time of employment. Preferences will be given to those with experiences in collider phenomenology, machine learning
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opportunities for methodological and/or software development, as well as the integration of machine learning into the project, depending on the candidate’s interests. This position is one of three Postdoctoral
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(SHORES) and the Division of Engineering, New York University Abu Dhabi, seek to recruit a Postdoctoral Associate to work on a fascinating project focused on the development machine-learning powered digital
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join the group to develop AI and machine learning based software to assist clinical workflow and pre-clinical studies. Required Qualifications: Ph.D. in a physical science or engineering field Strong
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projects as well as general research involving the application of methods from theoretical physics, mathematics, and machine learning with the goal to understand the brain function. Postdoctoral Fellowships
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for this position will work as a member of an interdisciplinary team led by Dr. Colin Xu on Department of Defense (DoD)-funded research project involving the use of statistical and machine learning methods