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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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Genomics at Harvard Medical School Several positions are available in the Park Lab (https://compbio.hms.harvard.edu/ ). The aim of the laboratory is to develop and apply innovative computational methods
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postdoctoral research associate to work with Professor Michael Desai at Harvard University on projects involving inferring sequence-function landscapes, using a combination of empirical data and ML methods (e.g
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sequence-function landscapes, using a combination of empirical data and ML methods (e.g. transformer models). One focus of this work will be on B-cell receptor evolution. Experience in applications of modern
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supervision of Prof. Flavio Calmon at Harvard SEAS. The postdoctoral researcher will develop information-theoretic methods for alignment, privacy, and reliability in modern AI systems. Basic Qualifications
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machine learning methods for computational materials physics and chemistry. Projects include: The aim is to develop generalized equivariant neural network models NequIP and Allegro for machine learned
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position. We are most interested in applicants who have experience in computational methods development, in human genetics or a different field. Possible areas of research include: 1. Developing methods
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conference each spring, and an organized faculty mentorship component. Residency at the Warren Center for the term of the appointment is therefore required. Fellows receive a salary and benefits.
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these methods. The appointment is for one year with possibility of renewal based on performance. The appointment will be funded by non-federal sources. Basic Qualifications: Candidates must have a PhD in a
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cutting-edge methods to visualize cellular organization. As one example, we are using time-resolved cryo-vitrification (both cryo-plunging and high-pressure freezing) to visualize the nanoscale dynamics