Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
, United States of America [map ] Subject Area: Evolutionary Anthropology Appl Deadline: 2026/01/31 11:59PM (posted 2025/10/06, listed until 2026/01/31) Position Description: Apply Position Description The Pontzer Lab in
-
research environment. Outstanding written and verbal communication skills are essential. The terms of employment are very competitive and include housing and educational subsidies for children. Applications
-
, speech, images, and physiological signals. Preferred Experience: The lab highly values candidates with one or more of the following experiences: Human-Centered Applications: Familiarity applying ML in
-
on schedule. Excellent (written and verbal) proficiency in English, good communication and leadership skills. Additional Information Benefits A meaningful job in a dynamic and ambitious university, in
-
to enhance learning and student success by advancing the health and well-being of our diverse University community. This mission is pursued and supports the University’s purpose by using current
-
communication abilities are essential. The candidate should be capable of working both independently and collaboratively within a team. Applicants must have a PhD in materials science and engineering, chemical
-
) About the Project Deep learning models, and in particular large language models (LLMs), have demonstrated remarkable capabilities but remain limited by their heavy computational requirements, lack
-
the Job related to staff position within a Research Infrastructure? No Offer Description Description The Division of Engineering and the Center for Interacting Urban Networks (CITIES) at New York
-
qualitative and quantitative research methods. Excellent communication skills in English and ability to collaborate with diverse stakeholders. A publication record in relevant scientific journals. Excellent
-
(ongoing PhD project). These pre-screened datasets will then be analyzed by various machine learning techniques (dimensionality reduction, unsupervised clustering, artificial neural networks, auto-encoders