Sort by
Refine Your Search
-
constructs and within-person change, as well as spoken narrative data. The overarching goal is to characterize longitudinal trajectories of core computational constructs (e.g., reward learning, decision-making
-
studies how the infant brain learns about the social world and how early-life experience shapes neural circuits with long-term consequences for learning, attachment, and mental health. Our work focuses on
-
Research Initiative on the History of Sexualities and is expected to participate in their activities and to teach one course during the fellowship. Compensation is commensurate with education and experience
-
Qualifications · PhD or MD/PhD in neuroscience, molecular/cell biology, bioengineering, or a related field. · Demonstrated experience in extracellular vesicle exosome biology and advanced statistical analysis
-
, design and analysis of virus-derived RNA libraries, and development of machine learning models for detecting functional elements in viral metagenomic datasets. This project is a collaboration with the
-
questions, with a track record of in-depth mechanistic interrogations evidence by publications. A successful candidate will likely have (a) a recent (<4 years) PhD degree in microbiology, immunology, genomics
-
looking for inquisitive, creative, and passionate researchers with a PhD, MD, or MD/PhD (or related field such as genetics, genomics, computational biology, biochemistry, machine learning, population
-
. Overview of the Postdoctoral Position This position is designed for a PhD, or equivalent doctoral-level investigator who seeks advanced research training at the intersection of cardiovascular medicine
-
. Required Qualifications PhD, MD/PhD, or equivalent doctoral degree by the start date Strong background in cell biology, molecular biology, biochemistry, metabolism, physiology, genetics, chemical biology, or
-
-scale human datasets. You will: - Build and apply machine learning and deep learning models to multi-scale (cells, brains, patients), multi-modal (omics, biosensor data, vision, electronic health data