Sort by
Refine Your Search
-
. Candidates should demonstrate track-records in microfluidics, Lab-on-Chip, and micro-fabrication and knowledge in molecular biology, diagnostics, and/or optical measurements is a plus. 2. Nanofabrication and
-
/neuropixel probes and electrical microstimulation to study attention and decision making networks in a behaving animal model together with parallel studies in humans. The project is part of a NIMH Silvio O
-
to excellence in education are encouraged to apply. A PhD in Materials Science, Optics, Physics, Chemistry, Electrical, Chemical, Mechanical, Civil or Bio Engineering or related area is required. We
-
models, programming, and quantitative methods. Preferred qualifications include experience in reinforcement learning, neural networks, and/or statistics. Questions can be addressed to Professor Nathaniel
-
of laminar/neuropixel probes and electrical microstimulation to study attention and decision making networks in a behaving animal model together with parallel studies in humans. The project is part of a NIMH
-
of epistemic values in scientific practice, or the expression of values in collective behaviors (e.g., in online social networks). The proposed research is expected to yield both theoretical and empirical
-
Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
to develop hybrid models for sea ice that combine coupled climate models and machine learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation
-
attention and decision making networks in a behaving animal model together with parallel studies in humans. The project is part of a NIMH Silvio O. Conte Center on the "Cognitive Thalamus". The successful
-
values in collective behaviors (e.g., in online social networks). The proposed research is expected to yield both theoretical and empirical publications. The candidate will be appointed in the Department
-
. Preferred qualifications include experience in reinforcement learning, neural networks, and/or statistics. Questions can be addressed to Professor Nathaniel Daw, ndaw@princeton.edu. Review of applications